Relaxing convolution infer checks.

- Weight dtype can be different than idtype. So, using the weight tensor to set
the dtype of weight.
- For conv2d NCHWc operator, the weight can be of any dimension. For int8
computation on Intel, it can be 7D. Relaxing the weight type checking.

src/relay/op/nn/convolution.cc

@@ -83,8 +83,12 @@ bool Conv2DRel(const Array<Type>& types,
     channels = param->channels;
     dilated_ksize_y = 1 + (param->kernel_size[0] - 1) * param->dilation[0];
     dilated_ksize_x = 1 + (param->kernel_size[1] - 1) * param->dilation[1];
+    DataType weight_dtype = data->dtype;
+    if (weight != nullptr) {
+      weight_dtype = weight->dtype;
+    }
     // assign result to reporter
-    reporter->Assign(types[1], TensorTypeNode::make(wshape, data->dtype));
+    reporter->Assign(types[1], TensorTypeNode::make(wshape, weight_dtype));
   } else {
     // use weight to infer the conv shape.
     if (weight == nullptr) return false;
@@ -701,7 +705,7 @@ RELAY_REGISTER_OP("nn.contrib_conv2d_NCHWc")
 .add_argument("data", "Tensor", "The input tensor.")
 .add_argument("weight", "Tensor", "The weight tensor.")
 .set_support_level(10)
-.add_type_rel("Conv2D", Conv2DRel)
+.add_type_rel("Conv2DNCHWc", Conv2DWinogradRel<Conv2DAttrs>)
 .set_attr<FInferCorrectLayout>("FInferCorrectLayout",
         Conv2DInferCorrectLayout<Conv2DAttrs>);