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>);
 

tests/python/relay/test_op_level2.py

@@ -45,7 +45,6 @@ def test_conv2d_infer_type():
         (2, 10, 3, 3), "float32")
 
     # infer by shape of w, mixed precision
-
     n, c, h, w = tvm.var("n"), 10, 224, 224
     x = relay.var("x", relay.TensorType((n, c, h, w), "int8"))
     w = relay.var("w", relay.TensorType((2, 10, 3, 3), "int8"))
@@ -55,6 +54,16 @@ def test_conv2d_infer_type():
     assert yy.checked_type ==  relay.TensorType(
         (n, 2, 222, 222), "int32")
 
+    # infer shape in case of differnt dtypes for input and weight.
+    n, c, h, w = tvm.var("n"), 10, 224, 224
+    x = relay.var("x", relay.TensorType((n, c, h, w), "uint8"))
+    w = relay.var("w", relay.TensorType((2, 10, 3, 3), "int8"))
+    y = relay.nn.conv2d(x, w, out_dtype="int32")
+    assert "out_dtype=\"int32\"" in y.astext()
+    yy = run_infer_type(y)
+    assert yy.checked_type ==  relay.TensorType(
+        (n, 2, 222, 222), "int32")
+
     # Infer with a different layout
     n, c, h, w = 4, 32, 224, 224
     x = relay.var("x", relay.TensorType((n//4, c//4, h, w, 4, 4), "int8"))