[Relay/TOPI][ONNX/TFLite] Refactor MATRIX_SET_DIAG Operator for Relay…

include/tvm/relay/attrs/transform.h

@@ -456,14 +456,10 @@ struct OneHotAttrs : public tvm::AttrsNode<OneHotAttrs> {
 
 /*! \brief Attributes used in matrix_set_diag operator */
 struct MatrixSetDiagAttrs : public tvm::AttrsNode<MatrixSetDiagAttrs> {
-  int k1;
-  int k2;
   bool super_diag_right_align;
   bool sub_diag_right_align;
 
   TVM_DECLARE_ATTRS(MatrixSetDiagAttrs, "relay.attrs.MatrixSetDiagAttrs") {
-    TVM_ATTR_FIELD(k1).set_default(0).describe("Lower limit (included) of the range of diagonals.");
-    TVM_ATTR_FIELD(k2).set_default(0).describe("Upper limit (included) of the range of diagonals.");
     TVM_ATTR_FIELD(super_diag_right_align)
         .set_default(true)
         .describe("Bool, true iff super-diagonal is right aligned (left-padded).");

include/tvm/topi/transform.h

@@ -1759,14 +1759,13 @@ inline Tensor sparse_to_dense(const Tensor& sparse_indices, const Array<PrimExpr
  * \param tag output tensor tag.
  * \return new tensor with given diagonal values.
  */
-inline Tensor matrix_set_diag(const Tensor& input, const Tensor& diagonal, int k1, int k2,
-                              bool super_diag_right_align, bool sub_diag_right_align,
+inline Tensor matrix_set_diag(const Tensor& input, const Tensor& diagonal, const Tensor& k1,
+                              const Tensor& k2, bool super_diag_right_align,
+                              bool sub_diag_right_align,
                               const std::string name = "T_matrix_set_diag",
                               const std::string tag = kInjective) {
   size_t ndim = input->shape.size() - 1;
 
-  bool only_one_diagonal = k1 == k2;
-
   return compute(
       input->shape,
       [&](const Array<Var>& iter_vars) {
@@ -1776,12 +1775,10 @@ inline Tensor matrix_set_diag(const Tensor& input, const Tensor& diagonal, int k
           for (size_t i = 0; i < ndim - 1; i++) {
             diagonal_indices.push_back(iter_vars[i]);
           }
-          if (only_one_diagonal) {
-            k = k1;
-          } else {
+          auto multi_diagonals = [&]() {
             // Determining which diagonal/sub-diagonal/super-diagonal it is
             k = iter_vars[ndim] - iter_vars[ndim - 1];
-            diagonal_indices.push_back(k2 - k);
+            diagonal_indices.push_back(k2(0) - k);
 
             // Calculating the offset in diagonal tensor for this diagonal
             auto get_offset = [&](PrimExpr M, PrimExpr N) {
@@ -1794,13 +1791,16 @@ inline Tensor matrix_set_diag(const Tensor& input, const Tensor& diagonal, int k
                                        : 0,
                 sub_diag_right_align ? get_offset(input->shape[ndim], input->shape[ndim - 1] + k)
                                      : 0);
-          }
+            return k;
+          };
+          auto get_k = [&]() { return if_then_else(k1(0) == k2(0), k1(0), multi_diagonals()); };
+          k = get_k();
           diagonal_indices.push_back(if_then_else(k >= 0, iter_vars[ndim - 1], iter_vars[ndim]) +
                                      offset);
           return diagonal(diagonal_indices);
         };
-        return if_then_else((PrimExpr)iter_vars[ndim] - iter_vars[ndim - 1] >= k1,
-                            if_then_else((PrimExpr)iter_vars[ndim] - iter_vars[ndim - 1] <= k2,
+        return if_then_else((PrimExpr)iter_vars[ndim] - iter_vars[ndim - 1] >= k1(0),
+                            if_then_else((PrimExpr)iter_vars[ndim] - iter_vars[ndim - 1] <= k2(0),
                                          get_diag(), input(iter_vars)),
                             input(iter_vars));
       },

python/tvm/relay/frontend/onnx.py

@@ -4226,6 +4226,35 @@ def _impl_v1(cls, inputs, attr, params):
         return _expr.TupleWrapper(_expr.Tuple(result), len(result))
 
 
+class Trilu(OnnxOpConverter):
+    """Operator converter for Trilu"""
+
+    @classmethod
+    def _impl_v14(cls, inputs, attr, params):
+        upper = attr.get("upper", 1)
+        input_shape = shape_of(inputs[0])
+        input_dims = infer_shape(input_shape)[0]
+        data_type = infer_type(inputs[0]).checked_type.dtype
+        k_tensor = relay.const(np.asarray([0], dtype=np.int64))
+        if len(inputs) == 2:
+            k_tensor = inputs[1]
+
+        diag_input = relay.zeros(fold_constant(shape_of(inputs[0])), dtype=data_type)
+
+        if upper == 0:
+            k1 = relay.add(k_tensor, relay.const(1, dtype="int64"))
+            k2 = relay.take(input_shape, relay.const(input_dims - 1, dtype="int32"))
+            k2 = relay.expand_dims(k2, axis=0)
+            return relay.matrix_set_diag(inputs[0], diag_input, k=(k1, k2))
+        else:
+            k1 = relay.take(input_shape, relay.const(input_dims-2, dtype="int32"))
+            k1 = relay.multiply(k1, relay.const(-1, dtype="int64"))
+            k1 = relay.subtract(k1, relay.const(1, dtype="int64"))
+            k1 = relay.expand_dims(k1, axis=0)
+            k2 = relay.subtract(k_tensor, relay.const(1, dtype="int64"))
+            return relay.matrix_set_diag(inputs[0], diag_input, k=(k1, k2))
+
+
 # compatible operators that do NOT require any conversion.
 _identity_list = []
 
@@ -4425,6 +4454,7 @@ def _get_convert_map(opset):
         "Adagrad": Adagrad.get_converter(opset),
         "Adam": Adam.get_converter(opset),
         "Momentum": Momentum.get_converter(opset),
+        "Trilu": Trilu.get_converter(opset),
     }
 
 

python/tvm/relay/frontend/tflite.py

@@ -3293,6 +3293,11 @@ def convert_matrix_set_diag(self, op):
 
         input_expr = self.get_tensor_expr(input_tensors[0])
         diagonal_expr = self.get_tensor_expr(input_tensors[1])
+        diag_shape = to_int_list(self.get_tensor_shape(input_tensors[1]))
+        input_shape = to_int_list(self.get_tensor_shape(input_tensors[0]))
+        if len(diag_shape) == len(input_shape) - 1:
+            diag_shape = np.insert(diag_shape, len(diag_shape)-1, 1)
+            diagonal_expr = _op.reshape(diagonal_expr, diag_shape)
 
         out = _op.matrix_set_diag(input_expr, diagonal_expr)
         return out
@@ -3313,13 +3318,16 @@ def convert_matrix_diag(self, op):
                     scale and zero points to be equal"
 
         shape = to_int_list(self.get_tensor_shape(diagonal))
-        shape = np.append(shape, shape[-1])
+        shape_copy = np.copy(shape)
+        diag_shape = np.insert(shape, len(shape)-1, 1).astype(np.int32)
+
+        shape = np.append(shape_copy, shape[-1]).astype(np.int32)
         dtype = self.get_tensor_type_str(diagonal.tensor.Type())
-
+              
         input_expr = _op.zeros(tuple(shape), dtype)
         diagonal_expr = self.get_tensor_expr(diagonal)
 
-        out = _op.matrix_set_diag(input_expr, diagonal_expr)
+        out = _op.matrix_set_diag(input_expr, _op.reshape(diagonal_expr, diag_shape))
         return out
 
     def convert_densify(self, op):

python/tvm/relay/op/transform.py

@@ -23,6 +23,7 @@
 from . import _make
 from .dyn import _make as _dyn_make
 from .tensor import shape_of
+import numpy as np
 
 
 def cast(data, dtype):
@@ -1352,6 +1353,11 @@ def matrix_set_diag(data, diagonal, k=0, align="RIGHT_LEFT"):
         k_one = k
         k_two = k
 
+    if not isinstance(k_one, Expr):
+        k_one = const(np.asarray([k_one], dtype=np.int64))
+    if not isinstance(k_two, Expr):
+        k_two = const(np.asarray([k_two], dtype=np.int64))
+
     super_diag_right_align = align[:5] == "RIGHT"
     sub_diag_right_align = align[-5:] == "RIGHT"
 

src/relay/op/tensor/transform.cc

@@ -3708,59 +3708,45 @@ TVM_REGISTER_NODE_TYPE(MatrixSetDiagAttrs);
 bool MatrixSetDiagRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                       const TypeReporter& reporter) {
   // `types` contains: [input, diagonal, result]
-  ICHECK_EQ(types.size(), 3);
+  ICHECK_EQ(types.size(), 5);
 
   const auto* input = types[0].as<TensorTypeNode>();
   ICHECK(input);
 
   const auto* diagonal = types[1].as<TensorTypeNode>();
   ICHECK(diagonal);
 
-  const auto param = attrs.as<MatrixSetDiagAttrs>();
-  ICHECK_GE(param->k2, param->k1);
-
-  int d_ndims = diagonal->shape.size();
-  int i_ndims = input->shape.size();
+  const auto* k1 = types[2].as<TensorTypeNode>();
+  ICHECK(k1);
 
-  reporter->Assert(input->shape[i_ndims - 2] > -param->k1);
-  reporter->Assert(input->shape[i_ndims - 1] > param->k2);
+  const auto* k2 = types[3].as<TensorTypeNode>();
+  ICHECK(k2);
 
+  int d_ndims = diagonal->shape.size(); 
   for (int i = 0; i < d_ndims - 2; i++) {
     reporter->AssertEQ(input->shape[i], diagonal->shape[i]);
   }
-  if (param->k1 != param->k2) {
-    reporter->AssertEQ(diagonal->shape[d_ndims - 2], param->k2 - param->k1 + 1);
-  } else if (d_ndims >= 2) {
-    reporter->AssertEQ(input->shape[d_ndims - 2], diagonal->shape[d_ndims - 2]);
-  }
-  auto max_diag_len = if_then_else(input->shape[i_ndims - 2] + (param->k2 > 0 ? param->k2 : 0) <=
-                                       input->shape[i_ndims - 1] + (param->k1 < 0 ? -param->k1 : 0),
-                                   input->shape[i_ndims - 2] + (param->k2 > 0 ? param->k2 : 0),
-                                   input->shape[i_ndims - 1] + (param->k1 < 0 ? -param->k1 : 0));
-  reporter->AssertEQ(diagonal->shape[d_ndims - 1], max_diag_len);
 
-  reporter->Assign(types[2], TensorType(input->shape, input->dtype));
+  reporter->Assign(types[4], TensorType(input->shape, input->dtype));
   return true;
 }
 
 Array<te::Tensor> MatrixSetDiagCompute(const Attrs& attrs, const Array<te::Tensor>& inputs,
                                        const Type& out_type) {
   const auto* param = attrs.as<MatrixSetDiagAttrs>();
   ICHECK(param != nullptr);
-  return Array<te::Tensor>{topi::matrix_set_diag(inputs[0], inputs[1], param->k1, param->k2,
+  return Array<te::Tensor>{topi::matrix_set_diag(inputs[0], inputs[1], inputs[2], inputs[3],
                                                  param->super_diag_right_align,
                                                  param->sub_diag_right_align)};
 }
 
-Expr MakeMatrixSetDiag(Expr input, Expr diagonal, int k1, int k2, bool super_diag_right_align,
+Expr MakeMatrixSetDiag(Expr input, Expr diagonal, Expr k1, Expr k2, bool super_diag_right_align,
                        bool sub_diag_right_align) {
   auto attrs = make_object<MatrixSetDiagAttrs>();
-  attrs->k1 = k1;
-  attrs->k2 = k2;
   attrs->super_diag_right_align = super_diag_right_align;
   attrs->sub_diag_right_align = sub_diag_right_align;
   static const Op& op = Op::Get("matrix_set_diag");
-  return Call(op, {input, diagonal}, Attrs(attrs), {});
+  return Call(op, {input, diagonal, k1, k2}, Attrs(attrs), {});
 }
 
 TVM_REGISTER_GLOBAL("relay.op._make.matrix_set_diag").set_body_typed(MakeMatrixSetDiag);
@@ -3776,9 +3762,11 @@ RELAY_REGISTER_OP("matrix_set_diag")
         **sub_diag_right_align** Bool, true iff sub-diagonal is right aligned (left-padded).
     )code" TVM_ADD_FILELINE)
     .set_attrs_type<MatrixSetDiagAttrs>()
-    .set_num_inputs(2)
+    .set_num_inputs(4)
     .add_argument("input", "Tensor", "Input Tensor.")
     .add_argument("diagonal", "Tensor", "Values to be filled in the diagonal.")
+    .add_argument("k1", "Tensor", "ILower limit (included) of the range of diagonals.")
+    .add_argument("k2", "Tensor", "Upper limit (included) of the range of diagonals.")
     .set_support_level(10)
     .add_type_rel("MatrixSetDiag", MatrixSetDiagRel)
     .set_attr<FTVMCompute>("FTVMCompute", MatrixSetDiagCompute)

src/topi/transform.cc

@@ -209,11 +209,10 @@ TVM_REGISTER_GLOBAL("topi.one_hot").set_body([](TVMArgs args, TVMRetValue* rv) {
 });
 
 TVM_REGISTER_GLOBAL("topi.matrix_set_diag").set_body([](TVMArgs args, TVMRetValue* rv) {
-  int k1 = args[2];
-  int k2 = args[3];
   bool super_diag_right_align = args[4];
   bool sub_diag_right_align = args[5];
-  *rv = matrix_set_diag(args[0], args[1], k1, k2, super_diag_right_align, sub_diag_right_align);
+  *rv = matrix_set_diag(args[0], args[1], args[2], args[3], super_diag_right_align,
+                        sub_diag_right_align);
 });
 
 TVM_REGISTER_GLOBAL("topi.adv_index").set_body([](TVMArgs args, TVMRetValue* rv) {

tests/python/frontend/onnx/test_forward.py

@@ -5773,6 +5773,47 @@ def repeat(N, D):
         repeat(2, D),
     )
 
+@tvm.testing.parametrize_targets
+def test_trilu(target, dev):
+    def verify_trilu(in_shape, k, upper):
+        trilu_node = helper.make_node('Trilu', inputs=["x", "k"], outputs=["out"], upper=upper)
+        graph = helper.make_graph(
+            [trilu_node],
+            "trilu_test",
+            inputs=[
+                helper.make_tensor_value_info("x", TensorProto.FLOAT, list(in_shape)),
+                helper.make_tensor_value_info("k", TensorProto.INT64, list((1,))),
+            ],
+            outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(in_shape))],
+        )
+
+        model = helper.make_model(graph, producer_name="trilu_test")
+        input_array = np.random.rand(*in_shape).astype("float32")
+        verify_with_ort_with_inputs(model, [input_array, np.asarray(k)], target=target, dev=dev, use_vm=True)
+
+    in_shape = (4, 5)
+    verify_trilu(in_shape, [4], 0)
+    verify_trilu(in_shape, [5], 0)
+    verify_trilu(in_shape, [5], 1)
+    verify_trilu(in_shape, [-1], 0)
+    verify_trilu(in_shape, [-1], 1)
+    verify_trilu(in_shape, [-7], 0)
+    verify_trilu(in_shape, [-7], 1)
+    verify_trilu(in_shape, [6], 0)
+    verify_trilu(in_shape, [6], 1)
+
+    in_shape = (3, 1, 5)
+    verify_trilu(in_shape, [0], 0)
+    verify_trilu(in_shape, [1], 1)
+    verify_trilu(in_shape, [6], 0)
+    verify_trilu(in_shape, [6], 1)
+
+    in_shape = (3, 5, 5)
+    verify_trilu(in_shape, [0], 0)
+    verify_trilu(in_shape, [0], 1)
+    verify_trilu(in_shape, [-1], 0)
+    verify_trilu(in_shape, [-1], 1)
+
 
 if __name__ == "__main__":
     test_flatten()
@@ -5864,3 +5905,4 @@ def repeat(N, D):
     test_convinteger()
     test_batch_matmul()
     test_global_lppool()
+    test_trilu()

tests/python/relay/test_op_level10.py

@@ -543,7 +543,13 @@ def test_matrix_set_diag():
     def _verify(input_shape, diagonal_shape, dtype, k=0, align="RIGHT_LEFT"):
         input = relay.var("input", relay.TensorType(input_shape, dtype))
         diagonal = relay.var("diagonal", relay.TensorType(diagonal_shape, dtype))
-        out = relay.matrix_set_diag(input, diagonal, k, align)
+        out = None
+        if len(diagonal_shape) == len(input_shape) - 1:
+            new_shape = list(diagonal_shape)
+            new_shape.insert(-1, 1)
+            out = relay.matrix_set_diag(input, relay.reshape(diagonal, new_shape), k, align)
+        else:
+            out = relay.matrix_set_diag(input, diagonal, k, align)
 
         in_type = run_infer_type(input)
         out_type = run_infer_type(out)

tests/python/topi/python/test_topi_transform.py

@@ -753,21 +753,36 @@ def check_device(target, dev):
 def verify_matrix_set_diag(input_shape, diagonal_shape, dtype, k=0, align="RIGHT_LEFT"):
     input = te.placeholder(shape=input_shape, name="input", dtype=dtype)
     diagonal = te.placeholder(shape=diagonal_shape, name="diagonal", dtype=dtype)
-    matrix_set_diag_result = topi.transform.matrix_set_diag(input, diagonal, k, align)
+    k1 = te.placeholder(shape=(1,), name="k1", dtype="int64")
+    k2 = te.placeholder(shape=(1,), name="k2", dtype="int64")
+    matrix_set_diag_result = topi.transform.matrix_set_diag(input, diagonal, (k1, k2), align)
+
+    k_one, k_two = None, None
+    if isinstance(k, (tuple, list)):
+        k_one = k[0]
+        if len(k) >= 2:
+            k_two = k[1]
+        else:
+            k_two = k[0]
+    else:
+        k_one = k
+        k_two = k
 
     def check_device(target, dev):
         dev = tvm.device(target, 0)
         print("Running on target: %s" % target)
         with tvm.target.Target(target):
             s = tvm.topi.testing.get_injective_schedule(target)(matrix_set_diag_result)
-        fn = tvm.build(s, [input, diagonal, matrix_set_diag_result], target, name="matrix_set_diag")
+        fn = tvm.build(s, [input, diagonal, k1, k2, matrix_set_diag_result], target, name="matrix_set_diag")
         input_npy = np.random.randint(-100, 100, size=input_shape).astype(dtype)
         diagonal_npy = np.random.randint(-100, 100, size=diagonal_shape).astype(dtype)
         out_npy = tvm.topi.testing.matrix_set_diag(input_npy, diagonal_npy, k, align)
         input_nd = tvm.nd.array(input_npy, dev)
         diagonal_nd = tvm.nd.array(diagonal_npy, dev)
+        k1_nd = tvm.nd.array(np.asarray([k_one]), dev)
+        k2_nd = tvm.nd.array(np.asarray([k_two]), dev)
         out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(matrix_set_diag_result.dtype), dev)
-        fn(input_nd, diagonal_nd, out_nd)
+        fn(input_nd, diagonal_nd, k1_nd, k2_nd, out_nd)
         out_topi = out_nd.numpy()
         tvm.testing.assert_allclose(out_topi, out_npy)
 
@@ -1235,9 +1250,6 @@ def test_sparse_to_dense():
 @tvm.testing.uses_gpu
 def test_matrix_set_diag():
     for dtype in ["float32", "int32"]:
-        verify_matrix_set_diag((2, 2), (2,), dtype)
-        verify_matrix_set_diag((4, 3, 3), (4, 3), dtype)
-        verify_matrix_set_diag((2, 3, 4), (2, 3), dtype, 1)
         verify_matrix_set_diag((2, 3, 4), (2, 4, 3), dtype, (-1, 2), "LEFT_RIGHT")
         verify_matrix_set_diag((2, 3, 4), (2, 4, 3), dtype, (-1, 2), "LEFT_LEFT")
         verify_matrix_set_diag((2, 3, 4), (2, 4, 3), dtype, (-1, 2), "RIGHT_RIGHT")