[ONNX] Make the ONNX Importer More Static

python/tvm/relay/frontend/common.py

@@ -491,6 +491,12 @@ def infer_type(node, mod=None):
     return ret
 
 
+def fold_constant(node, mod=None):
+    if mod is None:
+        mod = IRModule.from_expr(node)
+    return _transform.FoldConstantExpr(node, mod)
+
+
 def infer_channels(inputs, transpose=False):
     """A hack for getting 'channels' or 'units' since caffe2 does not provide
     these attributes. We check the shape of weights provided to get the number.

python/tvm/relay/op/image/image.py

@@ -17,7 +17,7 @@
 """Image operations."""
 from . import _make
 from ..dyn.image import _make as _dyn_make
-from ...expr import Expr
+from ...expr import Expr, Constant
 
 
 def resize(
@@ -66,6 +66,8 @@ def resize(
     result: relay.Expr
         The resized result.
     """
+    if isinstance(size, Constant):
+        size = list(size.data.asnumpy().astype("int32"))
     if isinstance(size, Expr):
         return _dyn_make.resize(
             data, size, layout, method, coordinate_transformation_mode, out_dtype

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

@@ -21,7 +21,7 @@
 from . import _make
 from ..dyn.nn import _make as _dyn_make
 from .utils import get_pad_tuple1d, get_pad_tuple2d, get_pad_tuple3d
-from ...expr import const, Expr
+from ...expr import const, Expr, Constant
 
 
 def conv1d(
@@ -1279,6 +1279,10 @@ def upsampling(
     result : tvm.relay.Expr
         The computed result.
     """
+    if isinstance(scale_h, Constant):
+        scale_h = scale_h.data.asnumpy().item()
+    if isinstance(scale_w, Constant):
+        scale_w = scale_w.data.asnumpy().item()
     if isinstance(scale_h, Expr) or isinstance(scale_w, Expr):
         if not isinstance(scale_h, Expr):
             scale_h = const(scale_h, "float64")
@@ -1338,6 +1342,12 @@ def upsampling3d(
     result : tvm.relay.Expr
         The computed result.
     """
+    if isinstance(scale_d, Constant):
+        scale_d = scale_d.data.asnumpy().item()
+    if isinstance(scale_h, Constant):
+        scale_h = scale_h.data.asnumpy().item()
+    if isinstance(scale_w, Constant):
+        scale_w = scale_w.data.asnumpy().item()
     if isinstance(scale_d, Expr) or isinstance(scale_h, Expr) or isinstance(scale_w, Expr):
         if not isinstance(scale_d, Expr):
             scale_d = const(scale_d, "float64")
@@ -1596,6 +1606,10 @@ def pad(data, pad_width, pad_value=0, pad_mode="constant"):
     result : tvm.relay.Expr
         The computed result.
     """
+    if isinstance(pad_value, Constant):
+        pad_value = pad_value.data.asnumpy().item()
+    if isinstance(pad_width, Constant):
+        pad_width = [list(i) for i in pad_width.data.asnumpy()]
     if isinstance(pad_width, Expr) or (isinstance(pad_value, Expr)):
         if not isinstance(pad_width, Expr):
             pad_width = const(list(pad_width))

python/tvm/relay/op/tensor.py

@@ -22,7 +22,7 @@
 
 from . import _make
 from .dyn import _make as _dyn_make
-from ..expr import Tuple, Expr
+from ..expr import Tuple, Expr, Constant
 from . import op as reg
 
 
@@ -960,6 +960,8 @@ def zeros(shape, dtype):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.zeros(shape, dtype)
     if isinstance(shape, int):
@@ -1001,6 +1003,8 @@ def ones(shape, dtype):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.ones(shape, dtype)
     if isinstance(shape, int):

python/tvm/relay/op/transform.py

@@ -21,7 +21,7 @@
 from . import _make
 from .dyn import _make as _dyn_make
 from .tensor import shape_of
-from ..expr import TupleWrapper, const, Expr, Tuple
+from ..expr import TupleWrapper, const, Constant, Expr, Tuple
 from ...tir import expr as _expr
 
 
@@ -216,6 +216,8 @@ def reshape(data, newshape):
     result : relay.Expr
         The reshaped result.
     """
+    if isinstance(newshape, Constant):
+        newshape = list(newshape.data.asnumpy())
     if isinstance(newshape, Expr):
         return _dyn_make.reshape(data, newshape)
     if isinstance(newshape, int):
@@ -431,6 +433,8 @@ def full(fill_value, shape=(), dtype=""):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.full(fill_value, shape, dtype)
     if isinstance(shape, int):
@@ -614,6 +618,8 @@ def tile(data, reps):
     data is promoted to be d-dimensional by prepending new axes.
     If data.ndim >=  d, reps is promoted to a.ndim by pre-pending 1's to it.
     """
+    if isinstance(reps, Constant):
+        reps = list(reps.data.asnumpy())
     if isinstance(reps, Expr):
         return _dyn_make.tile(data, reps)
     return _make.tile(data, reps)
@@ -753,6 +759,8 @@ def broadcast_to(data, shape):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.broadcast_to(data, shape)
     if isinstance(shape, int):
@@ -884,6 +892,12 @@ def strided_slice(data, begin, end, strides=None, slice_mode="end"):
         The computed result.
     """
     strides = strides or [1]
+    if isinstance(begin, Constant):
+        begin = list(begin.data.asnumpy())
+    if isinstance(end, Constant):
+        end = list(end.data.asnumpy())
+    if isinstance(strides, Constant):
+        strides = list(strides.data.asnumpy())
     if isinstance(begin, Expr) or isinstance(end, Expr) or isinstance(strides, Expr):
         if isinstance(begin, (tuple, list)):
             begin = const(list(begin))
@@ -1170,6 +1184,8 @@ def one_hot(indices, on_value, off_value, depth, axis, dtype):
              [0, 1, 0],
              [0, 0, 1]]
     """
+    if isinstance(depth, Constant):
+        depth = depth.data.asnumpy().item()
     if isinstance(depth, Expr):
         return _dyn_make.one_hot(indices, on_value, off_value, depth, axis, dtype)
     return _make.one_hot(indices, on_value, off_value, depth, axis, dtype)

python/tvm/relay/transform/transform.py

@@ -240,6 +240,23 @@ def LazyGradientInit():
     return _ffi_api.LazyGradientInit()
 
 
+def FoldConstantExpr(expr, mod):
+    """Fold the constant expressions in a Relay program.
+    Parameters
+    ----------
+    expr: Expr
+        The expression to fold
+    mod: IRModule
+        The module the expr lives in (for global calls)
+
+    Returns
+    -------
+    new_expr: Expr
+        The expr after Constant Folding
+    """
+    return _ffi_api.FoldConstantExpr(expr, mod)
+
+
 def FoldConstant():
     """Fold the constant expressions in a Relay program.
 

src/relay/transforms/fold_constant.cc

@@ -374,6 +374,8 @@ Expr FoldConstant(const Expr& expr, const IRModule& mod) {
   return ConstantFolder(mod).Mutate(expr);
 }
 
+TVM_REGISTER_GLOBAL("relay._transform.FoldConstantExpr").set_body_typed(FoldConstant);
+
 namespace transform {
 
 Pass FoldConstant() {

tests/python/relay/test_op_grad_level3.py

@@ -146,7 +146,7 @@ def test_zeros_ones_grad_const_ints():
 
 def test_zeros_ones_grad_const_expr():
     # when shape is static (i.e. not an input), there is no gradient at all
-    shape_const = relay.const(np.array([2, 3, 4]), dtype="int32")
+    shape_const = relay.const(np.array([2, 3, 4]), dtype="int32") * relay.const(1, dtype="int32")
     static_ty = relay.TensorType([2, 3, 4], dtype="float32")
     dyn_ty = relay.TensorType([relay.Any(), relay.Any(), relay.Any()], dtype="float32")
     expected_ty_static = relay.TupleType([static_ty, relay.TupleType([])])