[Relax] Validate StructInfo annotations in well-formed check

src/relax/analysis/well_formed.cc

@@ -362,6 +362,49 @@ class WellFormedChecker : public relax::ExprVisitor,
                                           << err.what());
       }
     }
+
+    if (check_struct_info_ && call->struct_info_.defined()) {
+      // The `InferStructInfo` method isn't currently exposed by the
+      // Normalizer, and can only be called indirectly by normalizing
+      // an expression that does not yet have `StructInfo`.
+      auto dummy_builder = tvm::relax::BlockBuilder::Create(mod_);
+      Call copied(call->op, call->args, call->attrs, call->sinfo_args);
+      Optional<Expr> normalized = NullOpt;
+      try {
+        normalized = dummy_builder->Normalize(copied);
+      } catch (std::exception& err) {
+        Malformed(Diagnostic::Error(call)
+                  << "Each Relax expression must be able to have its StructInfo inferred.  "
+                  << "However, inferring the struct info of expression " << GetRef<Call>(call)
+                  << " resulted in the error: \n"
+                  << err.what());
+      }
+      if (normalized.defined()) {
+        auto inferred_struct_info = GetStructInfo(normalized.value());
+        auto current_struct_info = Downcast<StructInfo>(call->struct_info_);
+
+        // An error should be raised if the annotated StructInfo is
+        // provably incorrect.  This check is done using
+        // `StructInfoBaseCheck(...) < kFailL1`, because `kFailL1`
+        // represents cases that are neither provably correct nor
+        // provably incorrect.  If this check were replaced with
+        // `!IsBaseOf(...)`, cases that are correct but not provably
+        // so would raise an exception.
+        //
+        // For example, if a dynamic size in the inferred StructInfo
+        // is equivalent to the expression used in the annotated
+        // StructInfo, but the TIR simplifications are not sufficient
+        // to prove that the two expressions are equivalent, we should
+        // not raise an error.
+        if (StructInfoBaseCheck(current_struct_info, inferred_struct_info) <
+            BaseCheckResult::kFailL1) {
+          Malformed(Diagnostic::Error(call)
+                    << "All information in StructInfo annotations must be correct.  "
+                    << "However, while the expression " << GetRef<Call>(call) << " is annotated as "
+                    << current_struct_info << ", the expression outputs " << inferred_struct_info);
+        }
+      }
+    }
   }
 
   void VisitExpr_(const IfNode* op) final {

src/relax/op/op.cc

@@ -1021,14 +1021,19 @@ StructInfo ReturnTensorToShapeStructInfo(const Call& call, const BlockBuilder& c
   ICHECK(call->args.size() == 1);
   ICHECK(call->args[0]->struct_info_.defined());
   const auto* tsinfo = GetStructInfoAs<TensorStructInfoNode>(call->args[0]);
-  ICHECK(tsinfo && tsinfo->shape.defined());
-  ShapeExpr shape_expr = Downcast<ShapeExpr>(tsinfo->shape.value());
-  ICHECK(shape_expr->values.size() == 1) << "relax.tensor_to_shape expected argument to be 1-d, "
-                                         << "but " << call << " has argument " << call->args[0]
-                                         << " with struct info " << call->args[0]->struct_info_;
-  const IntImmNode* ndim = shape_expr->values[0].as<IntImmNode>();
-  ICHECK(ndim);
-  return ShapeStructInfo(ndim->value);
+  ICHECK(tsinfo);
+  ICHECK_EQ(tsinfo->ndim, 1) << "relax.tensor_to_shape expected argument to be 1-d, "
+                             << "but " << call << " has argument " << call->args[0]
+                             << " with struct info " << call->args[0]->struct_info_;
+
+  if (tsinfo->shape.defined()) {
+    ShapeExpr shape_expr = Downcast<ShapeExpr>(tsinfo->shape.value());
+    const IntImmNode* ndim = shape_expr->values[0].as<IntImmNode>();
+    if (ndim) {
+      return ShapeStructInfo(ndim->value);
+    }
+  }
+  return ShapeStructInfo(kUnknownNDim);
 }
 
 RELAY_REGISTER_OP("relax.tensor_to_shape")

tests/python/relax/test_analysis_well_formed.py

@@ -1295,5 +1295,90 @@ def test_var_binding_with_incomplete_struct_info_must_be_consistent():
     assert not rx.analysis.well_formed(main)
 
 
+def test_incomplete_struct_info_must_be_consistent():
+    """StructInfo annotations must be accurate
+
+    Even though StructInfo annotation may be less specific, the
+    information that they do contain must be correct.
+
+    """
+
+    @I.ir_module(check_well_formed=False)
+    class Module:
+        @R.function
+        def main(
+            A: R.Tensor(shape=[128, 32], dtype="float32"),
+            B: R.Tensor(shape=[128, 32], dtype="float32"),
+        ):
+            C: R.Tensor(ndim=3) = R.add(A, B)
+            return C
+
+    assert not rx.analysis.well_formed(Module)
+
+
+def test_struct_info_annotations_must_be_correct():
+    """StructInfo annotations must be correct
+
+    To be well-formed, the inferred struct info must not conflict with
+    the StructInfo annotations.
+
+    """
+
+    @I.ir_module(check_well_formed=False)
+    class Module:
+        @R.function
+        def main(
+            A: R.Tensor(shape=[128, 32], dtype="float32"),
+            B: R.Tensor(shape=[128, 32], dtype="float32"),
+        ):
+            C: R.Tensor(shape=[128, 32], dtype="int32") = R.add(A, B)
+            return C
+
+    assert not rx.analysis.well_formed(Module)
+
+
+def test_struct_info_may_be_incomplete():
+    """StructInfo annotations may be less specific
+
+    The StructInfo annotations are not required to be an exact match
+    to the inferred StructInfo, and may provide less specific
+    information than the inference would provide.
+
+    """
+
+    @I.ir_module
+    class Module:
+        @R.function
+        def main(
+            A: R.Tensor(shape=[128, 32], dtype="float32"),
+            B: R.Tensor(shape=[128, 32], dtype="float32"),
+        ):
+            C: R.Object = R.add(A, B)
+            return C
+
+    assert rx.analysis.well_formed(Module)
+
+
+def test_incomplete_struct_info_must_be_consistent():
+    """StructInfo annotations must be accurate
+
+    Even though StructInfo annotation may be less specific, the
+    information that they do contain must be correct.
+
+    """
+
+    @I.ir_module(check_well_formed=False)
+    class Module:
+        @R.function
+        def main(
+            A: R.Tensor(shape=[128, 32], dtype="float32"),
+            B: R.Tensor(shape=[128, 32], dtype="float32"),
+        ):
+            C: R.Tensor(ndim=3) = R.add(A, B)
+            return C
+
+    assert not rx.analysis.well_formed(Module)
+
+
 if __name__ == "__main__":
     tvm.testing.main()

tests/python/relax/test_ast_printer.py

@@ -366,8 +366,8 @@ def f(
     ) -> R.Object:
         m = T.int64()
         z: R.Tensor((32, m), "float32") = R.multiply(x, y)
-        w: R.Tensor = R.multiply(z, z)
-        q: R.Tensor(ndim=2) = R.add(w, w)
+        w: R.Tensor(ndim=2) = R.multiply(z, z)
+        q: R.Tensor = R.add(w, w)
         t = R.add(w, z)
         sh: R.Shape = R.shape_of(t)
         o: R.Object = R.call_packed(

tests/python/relax/test_frontend_from_fx.py

@@ -79,7 +79,7 @@ def main(
                     out_layout="NCW",
                     out_dtype="float32",
                 )
-                lv2: R.Tensor((1, 6, 1)) = R.reshape(w2, [1, 6, 1])
+                lv2: R.Tensor((1, 6, 1), dtype="float32") = R.reshape(w2, [1, 6, 1])
                 lv3: R.Tensor((1, 6, 4), dtype="float32") = R.add(lv1, lv2)
                 gv: R.Tensor((1, 6, 4), dtype="float32") = lv3
                 R.output(gv)
@@ -171,7 +171,7 @@ def main(
                     out_layout="NCW",
                     out_dtype="float32",
                 )
-                lv2: R.Tensor((1, 6, 1)) = R.reshape(w2, [1, 6, 1])
+                lv2: R.Tensor((1, 6, 1), dtype="float32") = R.reshape(w2, [1, 6, 1])
                 lv3: R.Tensor((1, 6, 6), dtype="float32") = R.add(lv1, lv2)
                 gv: R.Tensor((1, 6, 6), dtype="float32") = lv3
                 R.output(gv)
@@ -263,7 +263,7 @@ def main(
                     out_layout="NCHW",
                     out_dtype="float32",
                 )
-                lv2: R.Tensor((1, 6, 1, 1)) = R.reshape(w2, [1, 6, 1, 1])
+                lv2: R.Tensor((1, 6, 1, 1), dtype="float32") = R.reshape(w2, [1, 6, 1, 1])
                 lv3: R.Tensor((1, 6, 4, 4), dtype="float32") = R.add(lv1, lv2)
                 gv: R.Tensor((1, 6, 4, 4), dtype="float32") = lv3
                 R.output(gv)
@@ -355,7 +355,7 @@ def main(
                     out_layout="NCHW",
                     out_dtype="float32",
                 )
-                lv2: R.Tensor((1, 3, 1, 1)) = R.reshape(w2, [1, 3, 1, 1])
+                lv2: R.Tensor((1, 3, 1, 1), dtype="float32") = R.reshape(w2, [1, 3, 1, 1])
                 lv3: R.Tensor((1, 3, 16, 16), dtype="float32") = R.add(lv1, lv2)
                 gv: R.Tensor((1, 3, 16, 16), dtype="float32") = lv3
                 R.output(gv)
@@ -447,7 +447,7 @@ def main(
                     out_layout="NCDHW",
                     out_dtype="float32",
                 )
-                lv2: R.Tensor((1, 6, 1, 1, 1)) = R.reshape(w2, [1, 6, 1, 1, 1])
+                lv2: R.Tensor((1, 6, 1, 1, 1), dtype="float32") = R.reshape(w2, [1, 6, 1, 1, 1])
                 lv3: R.Tensor((1, 6, 4, 4, 4), dtype="float32") = R.add(lv1, lv2)
                 gv: R.Tensor((1, 6, 4, 4, 4), dtype="float32") = lv3
                 R.output(gv)

tests/python/relax/test_transform_decompose_ops.py

@@ -360,14 +360,14 @@ def test_op_tensor_to_shape():
     @I.ir_module
     class Before:
         @R.function
-        def main(t: R.Tensor(ndim=1, dtype="int64")):
+        def main(t: R.Tensor([3], dtype="int64")):
             gv: R.Shape(ndim=3) = R.tensor_to_shape(t)
             return gv
 
     @I.ir_module
     class Expected:
         @R.function
-        def main(t: R.Tensor(dtype="int64", ndim=1)) -> R.Shape(ndim=3):
+        def main(t: R.Tensor([3], dtype="int64")) -> R.Shape(ndim=3):
             x = T.int64()
             x_1 = T.int64()
             x_2 = T.int64()

tests/python/relax/test_transform_ipc_allreduce_rewrite.py

@@ -83,7 +83,7 @@ def main(shape: R.Shape(["m", "n"])):  # type: ignore
             alloc: R.Tensor((m, n), dtype="float16") = R.builtin.alloc_tensor(  # type: ignore
                 R.shape([m, n]), R.dtype("float16"), R.prim_value(0), R.str("global")
             )
-            lv1: R.Tensor((m, n), dtype="float16") = R.reshape(alloc, (m * n,))  # type: ignore
+            lv1: R.Tensor((m * n,), dtype="float16") = R.reshape(alloc, (m * n,))  # type: ignore
             alloc1: R.Tensor((m * n,), dtype="float16") = R.builtin.alloc_tensor(  # type: ignore
                 R.shape([m * n]), R.dtype("float16"), R.prim_value(0), R.str("global")
             )
@@ -103,7 +103,7 @@ def main(
             alloc: R.Tensor((m, n), dtype="float16") = R.builtin.alloc_tensor(  # type: ignore
                 R.shape([m, n]), R.dtype("float16"), R.prim_value(0), R.str("ipc_memory")
             )
-            lv1: R.Tensor((m, n), dtype="float16") = R.reshape(  # type: ignore
+            lv1: R.Tensor((m * n,), dtype="float16") = R.reshape(  # type: ignore
                 alloc, R.shape([m * n])
             )
             alloc1: R.Tensor((m * n,), dtype="float16") = R.builtin.alloc_tensor(  # type: ignore

tests/python/relax/test_transform_legalize_ops_ccl.py

@@ -101,8 +101,8 @@ def test_scatter_from_worker0():
     @tvm.script.ir_module
     class ScatterFromWorker0:
         @R.function
-        def main(x: R.Tensor((10, 10), "float32"))  -> R.Tensor((5, 10), "float32"):
-            gv0: R.Tensor((5, 10), "float32") = R.ccl.scatter_from_worker0(x, num_workers=2, axis=1)
+        def main(x: R.Tensor((10, 10), "float32"))  -> R.Tensor((10,5), "float32"):
+            gv0: R.Tensor((10,5), "float32") = R.ccl.scatter_from_worker0(x, num_workers=2, axis=1)
             return gv0
 
     @I.ir_module

tests/python/relax/test_transform_legalize_ops_create_datatype.py

@@ -160,19 +160,19 @@ def test_full_like():
     @tvm.script.ir_module
     class FullLike:
         @R.function
-        def main(x: R.Tensor((2, 3), "int32"), v: R.Tensor((), "float32")) -> R.Tensor((2, 3), "float32"):
-            gv: R.Tensor((2, 3), "float32") = R.full_like(x, v)
+        def main(x: R.Tensor((2, 3), "int32"), v: R.Tensor((), "float32")) -> R.Tensor((2, 3), "int32"):
+            gv: R.Tensor((2, 3), "int32") = R.full_like(x, v)
             return gv
 
     @tvm.script.ir_module
     class Expected:
         @R.function
-        def main(x: R.Tensor((2, 3), "int32"), v: R.Tensor((), "float32")) -> R.Tensor((2, 3), "float32"):
-            gv = R.call_tir(Expected.full, (v,), R.Tensor((2, 3), dtype="float32"))
+        def main(x: R.Tensor((2, 3), "int32"), v: R.Tensor((), "float32")) -> R.Tensor((2, 3), "int32"):
+            gv = R.call_tir(Expected.full, (v,), R.Tensor((2, 3), dtype="int32"))
             return gv
 
         @T.prim_func(private=True)
-        def full(rxplaceholder: T.Buffer((), "float32"), T_full: T.Buffer((T.int64(2), T.int64(3)), "float32")):
+        def full(rxplaceholder: T.Buffer((), "float32"), T_full: T.Buffer((T.int64(2), T.int64(3)), "int32")):
             T.func_attr({"tir.noalias": True})
             for i0, i1 in T.grid(T.int64(2), T.int64(3)):
                 with T.block("T_full"):
@@ -191,26 +191,26 @@ def test_full_like_constant_scalar_fill_value():
     @tvm.script.ir_module
     class FullLike:
         @R.function
-        def main(x: R.Tensor((2, 3), "int32")) -> R.Tensor((2, 3), "float32"):
-            gv: R.Tensor((2, 3), "float32") = R.full_like(x, R.const(-5, "float32"))
+        def main(x: R.Tensor((2, 3), "int32")) -> R.Tensor((2, 3), "int32"):
+            gv: R.Tensor((2, 3), "int32") = R.full_like(x, R.const(-5, "float32"))
             return gv
 
     @tvm.script.ir_module
     class Expected:
         @R.function
-        def main(x: R.Tensor((2, 3), "int32")) -> R.Tensor((2, 3), "float32"):
-            gv = R.call_tir(Expected.full, R.tuple(), R.Tensor((2, 3), dtype="float32"))
+        def main(x: R.Tensor((2, 3), "int32")) -> R.Tensor((2, 3), "int32"):
+            gv = R.call_tir(Expected.full, R.tuple(), R.Tensor((2, 3), dtype="int32"))
             return gv
 
         @T.prim_func(private=True)
-        def full(T_full: T.Buffer((T.int64(2), T.int64(3)), "float32")):
+        def full(T_full: T.Buffer((T.int64(2), T.int64(3)), "int32")):
             T.func_attr({"tir.noalias": True})
             for i0, i1 in T.grid(T.int64(2), T.int64(3)):
                 with T.block("T_full"):
                     ax0, ax1 = T.axis.remap("SS", [i0, i1])
                     T.reads()
                     T.writes(T_full[ax0, ax1])
-                    T_full[ax0, ax1] = T.float32(-5)
+                    T_full[ax0, ax1] = T.int32(-5)
     # fmt: on
 
     mod = LegalizeOps()(FullLike)
@@ -253,33 +253,33 @@ def test_full_like_symbolic():
     @tvm.script.ir_module
     class FullLike:
         @R.function
-        def main(x: R.Tensor(("m", "n"), "int32"), v: R.Tensor((), "float32")) -> R.Tensor(("m", "n"), "float32"):
+        def main(x: R.Tensor(("m", "n"), "int32"), v: R.Tensor((), "float32")) -> R.Tensor(("m", "n"), "int32"):
             m = T.int64()
             n = T.int64()
-            gv: R.Tensor((m, n), "float32") = R.full_like(x, v)
+            gv: R.Tensor((m, n), "int32") = R.full_like(x, v)
             return gv
 
     @tvm.script.ir_module
     class Expected:
         @R.function
-        def main(x: R.Tensor(("m", "n"), "int32"), v: R.Tensor((), "float32")) -> R.Tensor(("m", "n"), "float32"):
+        def main(x: R.Tensor(("m", "n"), "int32"), v: R.Tensor((), "float32")) -> R.Tensor(("m", "n"), "int32"):
             m = T.int64()
             n = T.int64()
-            gv = R.call_tir(Expected.full, (v,), R.Tensor((m, n), dtype="float32"))
+            gv = R.call_tir(Expected.full, (v,), R.Tensor((m, n), dtype="int32"))
             return gv
 
         @T.prim_func(private=True)
         def full(rxplaceholder: T.Buffer((), "float32"), var_T_full: T.handle):
             T.func_attr({"tir.noalias": True})
             m = T.int64()
             n = T.int64()
-            T_full = T.match_buffer(var_T_full, [m, n], dtype="float32")
+            T_full = T.match_buffer(var_T_full, [m, n], dtype="int32")
             for i0, i1 in T.grid(m, n):
                 with T.block("T_full"):
                     ax0, ax1 = T.axis.remap("SS", [i0, i1])
                     T.reads(rxplaceholder[()])
                     T.writes(T_full[ax0, ax1])
-                    T_full[ax0, ax1] = rxplaceholder[()]
+                    T_full[ax0, ax1] = T.int32(rxplaceholder[()])
     # fmt: on
 
     mod = LegalizeOps()(FullLike)

tests/python/relax/test_transform_legalize_ops_index_linear_algebra.py

@@ -230,7 +230,7 @@ def test_strided_slice_no_strides():
     class StridedSlice:
         @R.function
         def main(x: R.Tensor((8, 9, 10, 10), "float32")) :
-            gv: R.Tensor((4, 9, 10, 3), "float32") = R.strided_slice(x, axes=[0, 1, 3], begin=[1, 0, 2], end=[8, 9, 4])
+            gv: R.Tensor((7, 9, 10, 2), "float32") = R.strided_slice(x, axes=[0, 1, 3], begin=[1, 0, 2], end=[8, 9, 4])
             return gv
 
     @tvm.script.ir_module