diff --git a/imperative/src/impl/ops/specializations.cpp b/imperative/src/impl/ops/specializations.cpp
index 681ad132..995149ec 100644
--- a/imperative/src/impl/ops/specializations.cpp
+++ b/imperative/src/impl/ops/specializations.cpp
@@ -61,6 +61,44 @@ auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     return opr::Dimshuffle::make(inputs[0], ds.pattern, 0UL, config);
 }
 
+std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
+        const OpDef& def, const SmallVector<LogicalTensorDesc>& inputs) {
+    auto&& ds = static_cast<const Dimshuffle&>(def);
+    mgb_assert(
+            ds.pattern.size() <= TensorShape::MAX_NDIM,
+            "Dimshuffle pattern exceeds max length of %zd", TensorShape::MAX_NDIM);
+    size_t nr_inp = inputs.size();
+    mgb_assert(nr_inp == 1, "Dimshuffle expects 1 inputs; got %lu actually", nr_inp);
+    auto&& src = inputs[0];
+    TensorShape out_shape;
+    if (src.layout.ndim == 0) {
+        return {{{TensorLayout(out_shape, src.layout.dtype), src.comp_node}}, false};
+    }
+    size_t pattern_ndim = *std::max_element(ds.pattern.begin(), ds.pattern.end()) + 1;
+    mgb_assert(
+            src.layout.ndim == pattern_ndim,
+            "input ndim mismatch for Dimshuffle: expect=%zd actual=%zd", pattern_ndim,
+            src.layout.ndim);
+    size_t idx = 0;
+    bool input_used[TensorLayout::MAX_NDIM] = {0};
+    for (auto i : ds.pattern) {
+        if (i < 0) {
+            out_shape[idx] = 1;
+        } else {
+            input_used[i] = true;
+            out_shape[idx] = src.layout.shape[i];
+        }
+        ++idx;
+    }
+    for (size_t i = 0; i < pattern_ndim; ++i) {
+        mgb_assert(
+                input_used[i] || src.layout.shape[i] == 1,
+                "non-1 dim discarded in Dimshuffle: ishp=%s dim=%zd",
+                src.layout.megdnn::TensorShape::to_string().c_str(), i);
+    }
+    return {{{TensorLayout(out_shape, src.layout.dtype), src.comp_node}}, true};
+}
+
 SmallVector<TensorPtr> apply_on_physical_tensor(
         const OpDef& def, const SmallVector<TensorPtr>& inputs,
         SmallVector<LogicalTensorDesc>& output_descs, const bool& validated) {
@@ -110,6 +148,7 @@ OP_TRAIT_REG(Dimshuffle, Dimshuffle, opr::Dimshuffle)
         .make_from_op_node(make_from_op_node)
         .apply_on_var_node(apply_on_var_node)
         .apply_on_physical_tensor(apply_on_physical_tensor)
+        .infer_output_attrs_fallible(infer_output_attrs_fallible)
         .fallback();
 }  // namespace dimshuffle
 }  // namespace
@@ -127,6 +166,22 @@ auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     return opr::AxisAddRemove::make(inputs[0], param, config);
 }
 
+std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
+        const OpDef& def, const SmallVector<LogicalTensorDesc>& inputs) {
+    auto&& op_def = def.cast_final_safe<AddAxis>();
+    size_t nr_inp = inputs.size();
+    mgb_assert(nr_inp == 1, "AddAxis expects 1 inputs; got %lu actually", nr_inp);
+    auto&& src = inputs[0];
+    auto olayout = src.layout;
+    if (src.layout.ndim == 0) {
+        return {{{TensorLayout(src.layout.dtype), src.comp_node}}, false};
+    }
+    for (auto&& i : op_def.axis) {
+        olayout.add_axis_cont_inplace(i);
+    }
+    return {{{olayout, src.comp_node}}, true};
+}
+
 SmallVector<TensorPtr> apply_on_physical_tensor(
         const OpDef& def, const SmallVector<TensorPtr>& inputs,
         SmallVector<LogicalTensorDesc>& output_descs, const bool& validated) {
@@ -145,6 +200,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
 OP_TRAIT_REG(AddAxis, AddAxis)
         .apply_on_var_node(apply_on_var_node)
         .apply_on_physical_tensor(apply_on_physical_tensor)
+        .infer_output_attrs_fallible(infer_output_attrs_fallible)
         .fallback();
 }  // namespace add_axis
 }  // namespace
@@ -188,9 +244,37 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
     return {Tensor::make(src->blob(), src->offset(), tlayout)};
 }
 
+std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
+        const OpDef& def, const SmallVector<LogicalTensorDesc>& inputs) {
+    auto&& op_def = def.cast_final_safe<RemoveAxis>();
+    size_t nr_inp = inputs.size();
+    mgb_assert(nr_inp == 1, "RemoveAxis expects 1 inputs; got %lu actually", nr_inp);
+    auto&& src = inputs[0];
+    auto olayout = src.layout;
+    if (src.layout.ndim == 0) {
+        return {{{TensorLayout(src.layout.dtype), src.comp_node}}, false};
+    }
+    for (auto&& i : op_def.axis) {
+        if (olayout.ndim == 1) {
+            mgb_assert(
+                    olayout.shape[0] == 1 && i == 0,
+                    "can not remove axis %u from tensor of shape=%s", i,
+                    olayout.megdnn::TensorShape::to_string().c_str());
+        } else {
+            mgb_assert(
+                    i < olayout.ndim && olayout.shape[i] == 1,
+                    "can not remove axis %u from tensor of shape=%s", i,
+                    olayout.megdnn::TensorShape::to_string().c_str());
+            olayout.remove_axis_inplace(i);
+        }
+    }
+    return {{{olayout, src.comp_node}}, true};
+}
+
 OP_TRAIT_REG(RemoveAxis, RemoveAxis)
         .apply_on_var_node(apply_on_var_node)
         .apply_on_physical_tensor(apply_on_physical_tensor)
+        .infer_output_attrs_fallible(infer_output_attrs_fallible)
         .fallback();
 }  // namespace remove_axis
 }  // namespace