[Relay, Topi] [Frontend][TFLite, MXNet] ReverseSequence operator (apa…

…che#5495) * TFLite reverse_sequence op * TFLite add_n implementation * reverse_sequence implementation * reverse_sequence implementation * reverse sequence * TOPI,Relay,TFLite - Reverse Sequence Signed-off-by: maheshambule <[email protected]> * Reverse Sequence small fixes Signed-off-by: maheshambule <[email protected]> * lint fixes Signed-off-by: maheshambule <[email protected]> * TFLite reverse_sequence op Signed-off-by: maheshambule * MXNet SequenceReverse implementation * clang format * clang format * review comment fixes
neo-ai · Jul 2, 2020 · f29e15b · f29e15b
1 parent f58d50a
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diff --git a/docs/api/python/topi.rst b/docs/api/python/topi.rst
@@ -46,6 +46,7 @@ List of operators
    topi.reinterpret
    topi.transpose
    topi.flip
+   topi.reverse_sequence
    topi.strided_slice
    topi.expand_dims
    topi.reshape
@@ -152,6 +153,7 @@ topi
 .. autofunction:: topi.reinterpret
 .. autofunction:: topi.transpose
 .. autofunction:: topi.flip
+.. autofunction:: topi.reverse_sequence
 .. autofunction:: topi.strided_slice
 .. autofunction:: topi.expand_dims
 .. autofunction:: topi.reshape

diff --git a/docs/langref/relay_op.rst b/docs/langref/relay_op.rst
@@ -132,6 +132,7 @@ This level enables additional math and transform operators.
    tvm.relay.repeat
    tvm.relay.tile
    tvm.relay.reverse
+   tvm.relay.reverse_sequence
    tvm.relay.unravel_index
    tvm.relay.sparse_to_dense
 

diff --git a/include/tvm/relay/attrs/transform.h b/include/tvm/relay/attrs/transform.h
@@ -194,6 +194,20 @@ struct ReverseAttrs : public tvm::AttrsNode<ReverseAttrs> {
   }
 };  // struct ReverseAttrs
 
+/*! \brief Attributes used in reverse_sequence operators */
+struct ReverseSequenceAttrs : public tvm::AttrsNode<ReverseSequenceAttrs> {
+  Integer seq_axis;
+  Integer batch_axis;
+
+  TVM_DECLARE_ATTRS(ReverseSequenceAttrs, "relay.attrs.ReverseSequenceAttrs") {
+    TVM_ATTR_FIELD(seq_axis).set_default(1).describe(
+        "The seq axis along which to reverse elements.");
+    TVM_ATTR_FIELD(batch_axis)
+        .set_default(0)
+        .describe("The batch axis along which to slice the tensor.");
+  }
+};  // struct ReverseSequenceAttrs
+
 /*! \brief Attributes used in squeeze operators */
 struct SqueezeAttrs : public tvm::AttrsNode<SqueezeAttrs> {
   // use axis to make the name numpy compatible.

diff --git a/python/tvm/relay/frontend/mxnet.py b/python/tvm/relay/frontend/mxnet.py
@@ -819,6 +819,21 @@ def _mx_reverse(inputs, attrs):
     return _op.reverse(inputs[0], **new_attrs)
 
 
+def _mx_sequence_reverse(inputs, attrs):
+    new_attrs = {}
+    use_seq_lengths = attrs.get_bool("use_sequence_length")
+    if not use_seq_lengths:
+        assert len(inputs) == 1
+        new_attrs["axis"] = attrs.get_int("axis")
+        return _op.reverse(inputs[0], **new_attrs)
+
+    assert len(inputs) == 2
+    new_attrs["seq_axis"] = attrs.get_int("axis")
+    # MXNet assumes batch_axis as 1.
+    new_attrs["batch_axis"] = 1
+    return _op.reverse_sequence(inputs[0], inputs[1], **new_attrs)
+
+
 def _mx_roi_align(inputs, attrs):
     new_attrs = {}
     new_attrs["pooled_size"] = attrs.get_int_tuple("pooled_size")
@@ -2078,6 +2093,7 @@ def impl(inputs, input_types):
     "take"          : _mx_take,
     "gather_nd"     : _mx_gather_nd,
     "reverse"       : _mx_reverse,
+    "SequenceReverse"  : _mx_sequence_reverse,
     "squeeze"       : _mx_squeeze,
     "broadcast_axis": _mx_broadcast_axis,
     "broadcast_axes": _mx_broadcast_axis,

diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py
@@ -130,6 +130,7 @@ def __init__(self, model, subgraph, exp_tab):
             'RESIZE_NEAREST_NEIGHBOR': self.convert_resize_nearest_neighbor,
             'ROUND': self.convert_round,
             'RSQRT': self.convert_rsqrt,
+            'REVERSE_SEQUENCE': self.convert_reverse_sequence,
             'SELECT': self.convert_select,
             'SHAPE': self.convert_shape,
             'SIN': self.convert_sin,
@@ -2002,6 +2003,33 @@ def convert_transpose(self, op):
 
         return out
 
+    def convert_reverse_sequence(self, op):
+        """Convert TFLite REVERSE_SEQUENCE"""
+        try:
+            from tflite.BuiltinOptions import BuiltinOptions
+            from tflite.ReverseSequenceOptions import ReverseSequenceOptions
+        except ImportError:
+            raise ImportError("The tflite package must be installed")
+
+        if self.is_quantized(op):
+            raise tvm.error.OpNotImplemented(
+                'TFLite does not support quantized REVERSE_SEQUENCE operator yet.')
+
+        input_tensors = self.get_input_tensors(op)
+        assert len(input_tensors) == 2, "input tensors length should be 2"
+
+        in_expr = self.get_tensor_expr(input_tensors[0])
+        length_expr = self.get_tensor_expr(input_tensors[1])
+
+        assert op.BuiltinOptionsType() == BuiltinOptions.ReverseSequenceOptions
+        op_options = op.BuiltinOptions()
+        options = ReverseSequenceOptions()
+        options.Init(op_options.Bytes, op_options.Pos)
+        batch_axis = options.BatchDim()
+        seq_axis = options.SeqDim()
+
+        return _op.reverse_sequence(in_expr, length_expr, seq_axis, batch_axis)
+
     def convert_cast(self, op):
         """Convert TFLite CAST"""
         try:
@@ -2700,14 +2728,10 @@ def has_expr(self, input_tensor_idx):
         return self.exp_tab.has_expr(get_tensor_name(self.subgraph, input_tensor_idx))
 
     def get_tensor_expr(self, tensor):
-        """ Returns constant expr for constant else a tensor expr"""
+        """ Return the Relay expr for tensor. """
         if self.has_expr(tensor.tensor_idx):
-            # In most cases, we can assume that TOCO fuses elemwise operators
-            # with constants - it means both will be tensors.
             expr = self.get_expr(tensor.tensor_idx)
         else:
-            # However, in some corner cases, the elemwise operator is not fused,
-            # we can receive as constant.
             type_str = self.get_tensor_type_str(tensor.tensor.Type())
             expr = self.exp_tab.new_const(self.get_tensor_value(tensor), dtype=type_str)
 

diff --git a/python/tvm/relay/op/_transform.py b/python/tvm/relay/op/_transform.py
@@ -41,6 +41,7 @@
 _reg.register_injective_schedule("full_like")
 _reg.register_injective_schedule("arange")
 _reg.register_injective_schedule("reverse")
+_reg.register_injective_schedule("reverse_sequence")
 _reg.register_injective_schedule("cast")
 _reg.register_injective_schedule("cast_like")
 _reg.register_injective_schedule("reinterpret")

diff --git a/python/tvm/relay/op/op_attrs.py b/python/tvm/relay/op/op_attrs.py
@@ -227,6 +227,10 @@ class TileAttrs(Attrs):
 class ReverseAttrs(Attrs):
     """Attributes used in reverse operators"""
 
+@tvm._ffi.register_object("relay.attrs.ReverseSequenceAttrs")
+class ReverseSequenceAttrs(Attrs):
+    """Attributes used in reverse sequence operators"""
+
 
 @tvm._ffi.register_object("relay.attrs.SqueezeAttrs")
 class SqueezeAttrs(Attrs):

diff --git a/python/tvm/relay/op/transform.py b/python/tvm/relay/op/transform.py
@@ -515,6 +515,53 @@ def reverse(data, axis):
     return _make.reverse(data, axis)
 
 
+def reverse_sequence(data, seq_lengths, seq_axis=1, batch_axis=0):
+    """Reverse the tensor for variable length slices.
+    Input is first sliced along batch axis and then elements are reversed along seq axis.
+
+    Parameters
+    ----------
+    data : relay.Expr
+        The tensor to be reversed.
+
+    seq_lengths : relay.Expr
+        A 1D Tensor with length a.dims[batch_axis]
+        Must be one of the following types: int32, int64
+        if seq_lengths[i] > a.dims[seq_axis], it is rounded to a.dims[seq_axis]
+        if seq_lengths[i] < 1, it is rounded to 1
+
+    seq_axis : int, optional
+        The axis along which the elements will be reversed. Default is 1.
+
+    batch_axis : int, optional
+        The axis along which the tensor will be sliced. Default is 0.
+
+    Returns
+    -------
+    ret : relay.Expr
+        The computed result of same shape and type as of input.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        x = [[0, 1, 2, 3],
+             [4, 5, 6, 7],
+             [8, 9, 10, 11],
+             [12, 13, 14, 15]]
+        relay.reverse(x, [1, 2, 3, 4], 0, 1) = [[0, 5, 10, 15],
+                                                [4, 1, 6, 11],
+                                                [8, 9, 2, 7],
+                                                [12, 13, 14, 3]]
+
+        relay.reverse(x, [1, 2, 3, 4], 1, 0) = [[0, 1, 2, 3],
+                                                [5, 4, 6, 7],
+                                                [10, 9, 8, 11],
+                                                [15, 14, 13, 12]]
+    """
+    return _make.reverse_sequence(data, seq_lengths, seq_axis, batch_axis)
+
+
 def where(condition, x, y):
     """Selecting elements from either x or y depending on the value of the
     condition.

diff --git a/src/relay/op/tensor/transform.cc b/src/relay/op/tensor/transform.cc
@@ -1397,7 +1397,8 @@ Array<te::Tensor> ReverseCompute(const Attrs& attrs, const Array<te::Tensor>& in
                                  const Type& out_type) {
   const ReverseAttrs* param = attrs.as<ReverseAttrs>();
   CHECK(param != nullptr);
-  return {topi::flip(inputs[0], param->axis)};
+  // pass empty seq_length tensor to reverse_sequence
+  return {topi::reverse_sequence(inputs[0], te::Tensor(), param->axis)};
 }
 
 Expr MakeReverse(Expr data, int axis) {
@@ -1423,6 +1424,96 @@ RELAY_REGISTER_OP("reverse")
     .set_attr<FTVMCompute>("FTVMCompute", ReverseCompute)
     .set_attr<TOpPattern>("TOpPattern", kInjective);
 
+// reverse sequence operator
+TVM_REGISTER_NODE_TYPE(ReverseSequenceAttrs);
+
+bool ReverseSequenceRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                        const TypeReporter& reporter) {
+  // `types` contains: [data, seq_lengths, result]
+  CHECK_EQ(types.size(), 3);
+  const auto* data = types[0].as<TensorTypeNode>();
+
+  if (data == nullptr) {
+    CHECK(types[0].as<IncompleteTypeNode>())
+        << "reverse_sequence: expect input type to be TensorType but get " << types[0];
+    return false;
+  }
+
+  const auto* seq_lengths = types[1].as<TensorTypeNode>();
+  if (seq_lengths == nullptr) {
+    CHECK(types[1].as<IncompleteTypeNode>())
+        << "reverse_sequence: expect input type to be TensorType but get " << types[1];
+    return false;
+  }
+
+  const int seq_lengths_dim = static_cast<int>(seq_lengths->shape.size());
+  CHECK(seq_lengths_dim == 1) << "For reverse_sequnece, seq_lengths must be a 1D vector";
+  CHECK(seq_lengths->dtype.is_int())
+      << "For reverse_sequnece, seq_lengths must be tensor of integer";
+
+  const auto* param = attrs.as<ReverseSequenceAttrs>();
+  const int ndim = static_cast<int>(data->shape.size());
+  int batch_axis = param->batch_axis;
+  CHECK(-ndim <= batch_axis && batch_axis < ndim)
+      << "reverse_sequence only accepts `batch_axis` in [-data.ndim, data.ndim - 1]"
+      << ", but got batch_axis = " << batch_axis << ", and data.ndim = " << ndim;
+
+  if (batch_axis < 0) {
+    batch_axis = static_cast<int>(data->shape.size()) + batch_axis;
+  }
+  CHECK(reporter->Assert(seq_lengths->shape[0] == data->shape[batch_axis]))
+      << "For reverse_sequnece seq_lengths size should match with dimension of batch axis"
+      << ", but got dimension of batch_axis = " << data->shape[batch_axis]
+      << ", and seq_length size = " << seq_lengths->shape[0];
+
+  const int seq_axis = param->seq_axis;
+  CHECK(-ndim <= seq_axis && seq_axis < ndim)
+      << "reverse_sequnece only accepts `seq_axis` in [-data.ndim, data.ndim - 1]"
+      << ", but got seq_axis = " << seq_axis << ", and data.ndim = " << ndim;
+
+  reporter->Assign(types[2], types[0]);
+  return true;
+}
+
+Array<te::Tensor> ReverseSequenceCompute(const Attrs& attrs, const Array<te::Tensor>& inputs,
+                                         const Type& out_type) {
+  const ReverseSequenceAttrs* param = attrs.as<ReverseSequenceAttrs>();
+  CHECK(param != nullptr);
+  return {topi::reverse_sequence(inputs[0], inputs[1], param->seq_axis, param->batch_axis)};
+}
+
+Expr MakeReverseSequence(Expr data, Expr seq_lengths, int seq_axis, int batch_axis) {
+  auto attrs = make_object<ReverseSequenceAttrs>();
+  attrs->seq_axis = seq_axis;
+  attrs->batch_axis = batch_axis;
+  static const Op& op = Op::Get("reverse_sequence");
+  return Call(op, {data, seq_lengths}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.reverse_sequence").set_body_typed(MakeReverseSequence);
+
+RELAY_REGISTER_OP("reverse_sequence")
+    .describe(R"code(Reverses the tensor for variable length slices.
+Input is first sliced along batch axis and then elements are reversed along seq axis.
+
+- **data**: The input data to the operator.
+
+- **seq_lengths**: A 1D Tensor with length data.dims[batch_axis].
+
+- **seq_axis**: The axis along which the elements will be reversed. Default is 1.
+
+- **batch_axis**: The axis along which the tensor will be sliced. Default is 0.
+
+)code" TVM_ADD_FILELINE)
+    .set_num_inputs(2)
+    .set_attrs_type<ReverseSequenceAttrs>()
+    .add_argument("data", "Tensor", "The input tensor.")
+    .add_argument("seq_lengths", "Tensor", "A 1D Tensor with length data.dims[batch_axis]")
+    .set_support_level(3)
+    .add_type_rel("ReverseSequence", ReverseSequenceRel)
+    .set_attr<FTVMCompute>("FTVMCompute", ReverseSequenceCompute)
+    .set_attr<TOpPattern>("TOpPattern", kInjective);
+
 // where operator
 bool WhereRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
               const TypeReporter& reporter) {

diff --git a/tests/python/frontend/mxnet/test_forward.py b/tests/python/frontend/mxnet/test_forward.py
@@ -472,6 +472,39 @@ def verify(x_shape, y_shape, axes):
     verify((3, 4), (2, 3), (0))
     verify((3, 4), (2, 3), (-1))
 
+def test_forward_sequence_reverse():
+    def verify(shape, seq_lengths, use_seq_lengths, seq_axis):
+        data_np = np.random.uniform(size=shape).astype("float32")
+
+        ref_res_args = [mx.nd.array(data_np), None, use_seq_lengths, seq_axis]
+        mx_sym_args = [mx.sym.var("data"), None, use_seq_lengths, seq_axis]
+        from_mxnet_args = [{"data": shape}, {"data": "float32"}]
+        in_data= [data_np]
+
+        if use_seq_lengths and seq_lengths:
+            seq_lengths_np = np.array(seq_lengths).astype("int32")
+            ref_res_args[1] = mx.nd.array(seq_lengths_np)
+            mx_sym_args[1] = mx.sym.var("seq_lengths")
+            from_mxnet_args[0].update({"seq_lengths": seq_lengths_np.shape})
+            from_mxnet_args[1].update({"seq_lengths": "int32"})
+            in_data.append(seq_lengths_np)
+
+        ref_res = mx.nd.SequenceReverse(*ref_res_args)
+        mx_sym = mx.sym.SequenceReverse(*mx_sym_args)
+        mod, _ = relay.frontend.from_mxnet(mx_sym, *from_mxnet_args)
+
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(*in_data)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())
+
+    verify((3, 4), [1, 2, 3, 1], True, 0)
+    verify((3, 4), None, False, 0)
+    verify((3, 5, 5, 6), [1, 2, 3, 1, 3], True, 0)
+    # MXNet accepts axis value as 0 only
+    # verify((3, 4, 5, 6), None, False, 2)
+
 def test_forward_l2_normalize():
     data = mx.sym.var('data')
     mx_sym = mx.sym.L2Normalization(data, mode="channel")
@@ -1232,6 +1265,7 @@ def verify(data_shape, kernel_size, max_displacement, stride1, stride2, pad_size
     test_forward_scalar_ops()
     test_forward_slice_like()
     test_forward_slice_axis()
+    test_forward_sequence_reverse()
     test_forward_l2_normalize()
     test_forward_shape_array()
     test_forward_squeeze()