[Relay, Topi] [TF, MXNet] Unravel Index operator

docs/api/python/topi.rst

@@ -47,6 +47,7 @@ List of operators
    topi.strided_slice
    topi.expand_dims
    topi.reshape
+   topi.unravel_index
    topi.squeeze
    topi.concatenate
    topi.split
@@ -147,6 +148,7 @@ topi
 .. autofunction:: topi.strided_slice
 .. autofunction:: topi.expand_dims
 .. autofunction:: topi.reshape
+.. autofunction:: topi.unravel_index
 .. autofunction:: topi.squeeze
 .. autofunction:: topi.concatenate
 .. autofunction:: topi.split

docs/frontend/tensorflow.rst

@@ -242,5 +242,6 @@ Supported Ops
 - Transpose
 - TruncateMod
 - Unpack
+- UnravelIndex
 - Where
 - ZerosLike

docs/langref/relay_op.rst

@@ -125,6 +125,7 @@ This level enables additional math and transform operators.
    tvm.relay.repeat
    tvm.relay.tile
    tvm.relay.reverse
+   tvm.relay.unravel_index
 
 
 **Level 4: Broadcast and Reductions**
@@ -299,6 +300,7 @@ Level 3 Definitions
 .. autofunction:: tvm.relay.repeat
 .. autofunction:: tvm.relay.tile
 .. autofunction:: tvm.relay.reverse
+.. autofunction:: tvm.relay.unravel_index
 
 
 Level 4 Definitions

include/tvm/relay/attrs/transform.h

@@ -321,6 +321,12 @@ struct ArgWhereAttrs : public tvm::AttrsNode<ArgWhereAttrs> {
   }
 };  // struct ArgWhereAttrs
 
+/*! \brief Attributes used in unravel_index operators */
+struct UnRavelIndexAttrs : public tvm::AttrsNode<UnRavelIndexAttrs> {
+  TVM_DECLARE_ATTRS(UnRavelIndexAttrs, "relay.attrs.UnRavelIndexAttrs") {
+  }
+};  // struct UnRavelIndexAttrs
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_TRANSFORM_H_

python/tvm/relay/frontend/mxnet.py

@@ -119,6 +119,13 @@ def impl(inputs, attrs):
     return impl
 
 
+def _mx_unravel_index(inputs, attrs):
+    assert len(inputs) == 1
+    shape = attrs.get_int_tuple("shape")
+    shape_expr = _expr.const(list(shape))
+    return _op.unravel_index(inputs[0], shape_expr)
+
+
 def _mx_zeros(inputs, attrs):
     assert len(inputs) == 0
     shape = attrs.get_int_tuple("shape")
@@ -1825,6 +1832,7 @@ def _get_bias_requantize_scale(_inputs, _data_scale, _kernel_scale):
     "Embedding"     : _mx_embedding,
     "argsort"       : _mx_argsort,
     "topk"          : _mx_topk,
+    "_unravel_index": _mx_unravel_index,
     "SequenceMask"  : _mx_sequence_mask,
     "SoftmaxOutput" : _mx_softmax_output,
     "SoftmaxActivation" : _mx_softmax_activation,

python/tvm/relay/frontend/tensorflow.py

@@ -626,6 +626,11 @@ def _impl(inputs, attr, params):
         return inputs[0]
     return _impl
 
+def _unravel_index():
+    def _impl(inputs, attr, params):
+        return _op.unravel_index(inputs[0], inputs[1])
+    return _impl
+
 def _crop_and_resize():
     def _impl(inputs, attr, params):
         # input image is a 4-D tensor of shape [batch, image_height, image_width, depth]
@@ -1736,6 +1741,7 @@ def _impl(inputs, attr, params):
     'Transpose'                         : _transpose(),
     'TruncateMod'                       : _elemwise('mod'),
     'Unpack'                            : _unpack(),
+    'UnravelIndex'                      : _unravel_index(),
     'Where'                             : _where(),
     'ZerosLike'                         : AttrCvt('zeros_like'),
 
@@ -2509,9 +2515,7 @@ def _parse_param(self, key, value, name, shape):
 
             array_ndim = len(np_array.shape)
             if array_ndim == 0:
-                new_array = np.empty([1], dtype=np_array.dtype)
-                new_array[0] = np_array
-                self._nodes[name] = [tvm.relay.const(new_array)]
+                self._nodes[name] = [tvm.relay.const(np_array)]
             else:
                 self._params[name] = tvm.nd.array(np_array)
                 self._nodes[name] = [_expr.var(name,

python/tvm/relay/op/_transform.py

@@ -54,6 +54,7 @@
 _reg.register_injective_schedule("sequence_mask")
 _reg.register_injective_schedule("one_hot")
 _reg.register_reduce_schedule("collapse_sum_like")
+_reg.register_injective_schedule("unravel_index")
 
 # concatenate
 _reg.register_schedule("concatenate", strategy.schedule_concatenate)

python/tvm/relay/op/transform.py

@@ -861,3 +861,26 @@ def one_hot(indices, on_value, off_value, depth, axis, dtype):
              [0, 0, 1]]
     """
     return _make.one_hot(indices, on_value, off_value, depth, axis, dtype)
+
+
+def unravel_index(indices, shape):
+    """Convert a flat index or array of flat indices into a tuple of coordinate arrays.
+
+    Example::
+    -   unravel_index([22, 41, 37], [7, 6]) = [[3, 6, 6],[4, 5, 1]]
+
+    Parameters
+    ----------
+    indices : relay.Expr
+        An integer array containing indices.
+
+    shape : relay.Expr
+        The shape of the array.
+
+    Returns
+    -------
+    result : relay.Expr
+        The tuple of coordinate arrays.
+    """
+
+    return _make.unravel_index(indices, shape)

src/relay/op/tensor/transform.cc

@@ -2703,5 +2703,71 @@ RELAY_REGISTER_OP("one_hot")
 .set_attr<FTVMCompute>("FTVMCompute", OneHotCompute)
 .set_attr<TOpPattern>("TOpPattern", kOutEWiseFusable);
 
+/* relay.unravel_index */
+TVM_REGISTER_NODE_TYPE(UnRavelIndexAttrs);
+
+bool UnRavelIndexRel(const Array<Type>& types,
+                     int num_inputs,
+                     const Attrs& attrs,
+                     const TypeReporter& reporter) {
+  CHECK_EQ(types.size(), 3);
+
+  const auto* indices = types[0].as<TensorTypeNode>();
+  if (indices == nullptr) {
+    CHECK(types[0].as<IncompleteTypeNode>())
+        << "unravel_index: expect input type to be TensorType but get " << types[0];
+    return false;
+  }
+  CHECK(indices->dtype.is_int()) << "indices of unravel_index must be tensor of integer";
+
+  const auto* shape = types[1].as<TensorTypeNode>();
+  if (shape == nullptr) {
+    CHECK(types[1].as<IncompleteTypeNode>())
+        << "unravel_index: expect input type to be TensorType but get " << types[1];
+    return false;
+  }
+  CHECK(indices->dtype.is_int()) << "shape of unravel_index must be tensor of integer";
+
+  Array<IndexExpr> indices_shape;
+  Array<IndexExpr> shape_shape;
+  indices_shape = indices->shape;
+  shape_shape = shape->shape;
+
+  Array<IndexExpr> oshape;
+  oshape.push_back(shape_shape[0]);
+  if (indices_shape.size() != 0) {
+    oshape.push_back(indices_shape[0]);
+  }
+  reporter->Assign(types[2], TensorType(oshape, indices->dtype));
+  return true;
+}
+
+Array<te::Tensor> UnRavelIndexCompute(const Attrs& attrs,
+                                      const Array<te::Tensor>& inputs,
+                                      const Type& out_type) {
+  return Array<te::Tensor>{topi::unravel_index(inputs[0], inputs[1])};
+}
+
+Expr MakeUnRavelIndex(Expr data, Expr shape) {
+  auto attrs = make_object<UnRavelIndexAttrs>();
+  static const Op& op = Op::Get("unravel_index");
+  return CallNode::make(op, {data, shape}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.unravel_index").set_body_typed(MakeUnRavelIndex);
+
+RELAY_REGISTER_OP("unravel_index")
+    .describe(
+        R"code(Converts a flat index or array of flat indices into a tuple of coordinate arrays.
+Example::
+    -   unravel_index([22, 41, 37], (7, 6)) = [[3, 6, 6], [4, 5, 1]]
+)code" TVM_ADD_FILELINE)
+    .set_num_inputs(2)
+    .set_attrs_type<UnRavelIndexAttrs>()
+    .set_support_level(3)
+    .add_type_rel("UnRavelIndexRel", UnRavelIndexRel)
+    .set_attr<FTVMCompute>("FTVMCompute", UnRavelIndexCompute)
+    .set_attr<TOpPattern>("TOpPattern", kInjective);
+
 }  // namespace relay
 }  // namespace tvm

tests/python/frontend/mxnet/test_forward.py

@@ -949,6 +949,32 @@ def verify(a_np, b_np):
     verify(np.asarray([4.0], 'float32'), np.asarray([3.0],'float32'))
 
 
+def test_forward_unravel_index():
+    def verify(x, shape, dtype):
+        a_np = np.array(x).astype(dtype)
+        mx_sym = _mx_symbol(mx.sym, 'unravel_index', [mx.sym.var('a'), shape])
+        ref_res = _mx_symbol(mx.nd, 'unravel_index', [mx.nd.array(a_np), shape])
+        shapes = {'a': a_np.shape}
+        mod, _ = relay.frontend.from_mxnet(mx_sym, shapes, dtype)
+
+        for target, ctx in ctx_list():
+            for kind in ["graph", "vm", "debug"]:
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(a_np)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())
+
+    for dtype in ["int32", "int64"]:
+        verify([0, 1, 2, 3], [2, 2], dtype)
+        verify([144, 13, 45], [6, 7, 10, 2], dtype)
+        verify([456], [6, 7, 10, 2], dtype)
+
+    # In below example, 5 is out of bound for array of size 4.
+    # MXNet implementation provides different result than TVM
+    # TVM implementation is inline with Tensorflow
+    # Ideally error should be thrown just like Numpy
+    # verify([0, 1, 2, 5], [2, 2], dtype)
+
+
 if __name__ == '__main__':
     test_forward_mlp()
     test_forward_vgg()
@@ -1003,4 +1029,5 @@ def verify(a_np, b_np):
     test_forward_convolution()
     test_forward_deconvolution()
     test_forward_cond()
-    test_forward_make_loss()
+    test_forward_make_loss()
+    test_forward_unravel_index()

tests/python/frontend/tensorflow/test_forward.py

@@ -3037,6 +3037,57 @@ def test_forward_add_n():
     _test_forward_add_n(in5)
 
 
+#######################################################################
+# Unravel Index
+# ----------------------
+def _test_forward_unravel_index(inputs):
+    tf.reset_default_graph()
+    with tf.Graph().as_default():
+        temp = []
+        for each in inputs:
+            temp.append(tf.placeholder(shape=each.shape, dtype=each.dtype))
+        output = tf.unravel_index(temp[0], temp[1])
+        compare_tf_with_tvm([each for each in inputs], [
+            each.name for each in temp], output.name)
+
+
+def _test_forward_unravel_index_scalar(x, y, dtype="int32"):
+    tf.reset_default_graph()
+    with tf.Graph().as_default():
+        indices_1 = constant_op.constant(x, dtype=dtype)
+        dims_1 = constant_op.constant(y, dtype=dtype)
+        out_1 = array_ops.unravel_index(indices_1, dims_1)
+        compare_tf_with_tvm([], [], out_1.name)
+
+
+def test_forward_unravel_index():
+    x = np.array([0, 1, 2, 3])
+    y = np.array([2, 2])
+    _test_forward_unravel_index([x, y])
+
+    x = np.array([0, 1, 2, 5])
+    y = np.array([2, 2])
+    _test_forward_unravel_index([x, y])
+
+    x = np.array([0, 1, 2, 5])
+    y = np.array([2])
+    _test_forward_unravel_index([x, y])
+
+    x = np.array([102, 300, 16])
+    y = np.array([10, 10, 9, 6])
+    _test_forward_unravel_index([x, y])
+
+    x = np.array([100])
+    y = np.array([10, 10, 9, 6])
+    _test_forward_unravel_index([x, y])
+
+    # Test scalar input
+    _test_forward_unravel_index_scalar(13, [1, 4, 5, 2])
+
+
+#######################################################################
+# Dilation2d
+# ----------------------
 def _test_dilation2d(tensor_in_sizes, filter_in_sizes,
                      strides, dilations, padding):
     """ One iteration of dilation2d with given shapes and attributes """
@@ -3151,6 +3202,7 @@ def test_forward_dilation():
     test_forward_squared_difference()
     test_forward_add_n()
     test_forward_floormod()
+    test_forward_unravel_index()
 
     # Reductions
     test_forward_argminmax()

tests/python/relay/test_op_level3.py

@@ -683,6 +683,44 @@ def verify_gather_nd(xshape, yshape, y_data):
     verify_gather_nd((3, 2, 2), (2, 2), [[0, 1], [1, 0]])
     verify_gather_nd((3, 2), (2, 2, 3), [[[0, 1, 2], [2, 0, 1]], [[0, 0, 0], [1, 1, 1]]])
 
+
+def test_unravel_index():
+    def verify_unravel_index(indices, shape, dtype):
+        x_data = np.array(indices).astype(dtype)
+        y_data = np.array(shape).astype(dtype)
+        x = relay.var("x", relay.TensorType(x_data.shape, dtype))
+        y = relay.var("y", relay.TensorType(y_data.shape, dtype))
+
+        z = relay.unravel_index(x, y)
+        zz = run_infer_type(z)
+
+        if len(x_data.shape) == 1:
+            out_shape = [y_data.shape[0], x_data.shape[0]]
+        else:
+            out_shape = [y_data.shape[0]]
+        assert zz.checked_type == relay.ty.TensorType(out_shape, dtype)
+
+        func = relay.Function([x, y], z)
+        ref_res = np.unravel_index(x_data, y_data)
+        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)(x_data, y_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+
+    for dtype in ["int64", "int32"]:
+        verify_unravel_index([0, 1, 2, 3], [2, 2], dtype)
+        verify_unravel_index([144], [5, 5, 5, 2], dtype)
+        verify_unravel_index(144, [5, 5, 5, 2], dtype)
+        verify_unravel_index([100, 13, 5], [5, 5, 5, 2], dtype)
+
+        # In below example, 5 is out of bound for array of size 4.
+        # Numpy implementation throws error for it
+        # TVM implementation does not throw error instead it produces
+        # output which is inline with Tensorflow
+        # verify_unravel_index([0, 1, 2, 5], [2, 2], dtype)
+
+
 if __name__ == "__main__":
     test_arange()
     test_cast()
@@ -713,3 +751,4 @@ def verify_gather_nd(xshape, yshape, y_data):
     test_tile()
     test_repeat()
     test_gather_nd()
+    test_unravel_index()