fix(mge/functional): fix convert_inputs before apply

GitOrigin-RevId: ab41974a1f

imperative/python/megengine/functional/nn.py

@@ -146,6 +146,7 @@ def conv2d(
         compute_mode=compute_mode,
         sparse=sparse_type,
     )
+    inp, weight = utils.convert_inputs(inp, weight)
     (output,) = apply(op, inp, weight)
     if bias is not None:
         output += bias
@@ -209,6 +210,7 @@ def conv_transpose2d(
         dilate_w=dilate_w,
         strategy=get_conv_execution_strategy(),
     )
+    weight, inp = utils.convert_inputs(weight, inp)
     (output,) = apply(op, weight, inp)
     if bias is not None:
         output += bias
@@ -243,6 +245,7 @@ def local_conv2d(
         dilate_w=dilate_w,
         # strategy=get_conv_execution_strategy(),
     )
+    inp, weight = utils.convert_inputs(inp, weight)
     (output,) = apply(op, inp, weight)
     if bias is not None:
         output += bias
@@ -900,6 +903,7 @@ def warp_perspective(
     op = builtin.WarpPerspective(
         imode=interp_mode, bmode=border_mode, format="NCHW", border_val=border_val
     )
+    inp, M = utils.convert_inputs(inp, M)
     (result,) = apply(op, inp, M, Tensor(dsize))
     return result
 
@@ -1004,6 +1008,7 @@ def matmul(
             format=format,
         )
 
+    inp1, inp2 = utils.convert_inputs(inp1, inp2)
     (result,) = apply(op, inp1, inp2)
     if shp is not None:
         result = result.reshape(shp)
@@ -1327,6 +1332,7 @@ def roi_pooling(
         output_shape = (output_shape, output_shape)
 
     op = builtin.ROIPooling(mode=mode, scale=scale)
+    inp, rois = utils.convert_inputs(inp, rois)
     result, _ = apply(
         op, inp, rois, Tensor(output_shape, dtype="int32", device=inp.device)
     )
@@ -1374,6 +1380,7 @@ def roi_align(
         sample_height=sample_height,
         sample_width=sample_width,
     )
+    input, rois = utils.convert_inputs(input, rois)
     result, *_ = apply(op, input, rois)
     return result
 

imperative/python/src/dispatcher.cpp

@@ -104,6 +104,10 @@ struct Dispatcher {
         auto& frame = stack.back();
         auto& mro = *frame.mro;
         auto& i = frame.mro_offset;
+        if (!mro.size()) {
+            PyErr_SetString(PyExc_NotImplementedError, "function not registered in dispatcher");
+            return nullptr;
+        }
         for (; i < mro.size(); ++i) {
             if (mro[i]->enabled) {
                 auto ret = caller(mro[i]->func);