fix(imperative): fix ConvBwdData layout issue

GitOrigin-RevId: e3657f8829adf2288e9822d7dfd7e281961bb081

imperative/python/test/unit/functional/test_functional.py

@@ -7,12 +7,14 @@ import numpy as np
 import pytest
 from utils import opr_test
 
+import megengine as mge
 import megengine.amp as amp
 import megengine.config as config
 import megengine.core.ops.builtin as builtin
 import megengine.core.tensor.dtype as dtype
 import megengine.functional as F
 import megengine.jit as jit
+import megengine.module as M
 from megengine import Parameter, Tensor, is_cuda_available, tensor
 from megengine.autodiff import GradManager
 from megengine.core._trace_option import use_symbolic_shape
@@ -1637,6 +1639,16 @@ def test_conv_transpose2d():
         output_shape.numpy(), np.array([20, 33, 94, 300], dtype=np.int32)
     )
 
+    @mge.jit.trace()
+    def func():
+        deconv = M.ConvTranspose2d(16, 33, (3, 5), (2, 3), (3, 4))
+        x = Tensor(np.random.rand(20, 16, 50, 100))
+        for i in range(20):
+            y = deconv(x._broadcast(F.concat([x.shape, x.shape])[:4]))
+        mge._sync()
+
+    func()
+
 
 def test_conv_transpose3d():
     m = ConvTranspose3d(

imperative/src/impl/ops/convolution.cpp

@@ -174,13 +174,15 @@ std::tuple<SmallVector<LogicalTensorDesc>, bool> infer_output_attrs_fallible(
     if (filter.ndim && diff.ndim) {
         // deduce_layout won't override existing dtype
         dnn_opr.opr().deduce_layout(filter, diff, output_layout);
-        if (inputs.size() == 3) {
-            if (!inputs[2].value.empty()) {
-                cg::copy_tensor_value_to_shape(output_layout, inputs[2].value);
-                output_layout.init_contiguous_stride();
-            } else {
-                output_layout.ndim = 0;
-            }
+    } else {
+        dnn_opr.opr().deduce_dtype(filter.dtype, diff.dtype, output_layout.dtype);
+    }
+    if (inputs.size() == 3) {
+        if (!inputs[2].value.empty()) {
+            cg::copy_tensor_value_to_shape(output_layout, inputs[2].value);
+            output_layout.init_contiguous_stride();
+        } else {
+            output_layout.ndim = 0;
         }
     }
     return {{{output_layout, inputs[0].comp_node}}, output_layout.ndim != 0};
@@ -202,8 +204,11 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
             if (inputs.size() == 3) {
                 cg::copy_tensor_value_to_shape(
                         out_layout, inputs[2]->get_value().proxy_to_default_cpu());
-                out_layout.init_contiguous_stride();
+            } else {
+                dnn_opr.op()->deduce_layout(
+                        inputs[0]->layout(), inputs[1]->layout(), out_layout);
             }
+            out_layout.init_contiguous_stride();
             return out_layout;
         }
     }();