[CLML][RELAY] Enable Pad and Conv2d layer fusion

python/tvm/relay/op/contrib/clml.py

@@ -147,6 +147,23 @@ def conv_pattern():
         pattern = pattern.optional(is_op("clip"))
         return pattern
 
+    def pad_conv_pattern():
+        """Create a pad with convolution pattern."""
+        pattern = is_op("nn.pad")(wildcard(), is_constant())
+        pattern = is_op("nn.conv2d")(pattern, is_constant())
+        pattern = pattern.optional(lambda x: is_op("nn.bias_add")(x, is_constant()))
+        pattern = pattern.optional(lambda x: is_op("add")(x, is_constant()))
+        pattern = pattern.optional(
+            lambda x: is_tuple_get_item(
+                is_op("nn.batch_norm")(
+                    x, is_constant(), is_constant(), is_constant(), is_constant()
+                )
+            )
+        )
+        pattern = pattern.optional(is_op("nn.relu"))
+        pattern = pattern.optional(is_op("clip"))
+        return pattern
+
     def batch_norm_pattern():
         """Create a batch norm pattern."""
         pattern = is_op("nn.batch_norm")(
@@ -200,9 +217,11 @@ def check_conv(extract):
 
         while call.op.name != "nn.conv2d":
             call = call.args[0]
+
         attrs, args = call.attrs, call.args
         if attrs.data_layout != "NCHW":
             return False
+
         if (
             (not clip_found)
             and (attrs.kernel_size[0] == 3)
@@ -211,6 +230,7 @@ def check_conv(extract):
             and (attrs.channels == attrs.groups)
         ):
             return False
+
         data_typ = args[0].checked_type
         kernel_typ = args[1].checked_type
         is_depthwise = is_depthwise_conv2d(
@@ -246,6 +266,7 @@ def check_default_op(extract):
         return True
 
     return [
+        ("clml.pad_conv2d", pad_conv_pattern(), check_conv),
         ("clml.conv2d", conv_pattern(), check_conv),
         ("clml.dense", dense_pattern(), check_default_op),
         ("clml.pad", pad_pattern(), check_pad_op),

src/relay/backend/contrib/clml/codegen.cc

@@ -83,7 +83,7 @@ class CLMLJSONSerializer : public backend::contrib::JSONSerializer {
     ICHECK(comp.defined()) << "CLML JSON runtime only supports composite functions.";
     const std::string name = comp.value();
     std::shared_ptr<JSONGraphNode> json_node;
-    if (name == "clml.conv2d") {
+    if (name == "clml.conv2d" || name == "clml.pad_conv2d") {
       json_node = CreateCompositeConvJSONNode(cn);
     } else if (name == "clml.batch_norm") {
       json_node = CreateBatchNormJSONNode(cn);

tests/python/contrib/test_clml/test_ops.py

@@ -45,7 +45,7 @@ def _get_conv_model(
     a = relay.var(next(iter(var)), shape=shape, dtype=dtype)
     input_arr = var[next(iter(var))]
     if has_pad:
-        p = ((0, 0), (padding[0], padding[0]), (padding[1], padding[1]), (0, 0))
+        p = ((0, 0), (0, 0), (padding[0], padding[0]), (padding[1], padding[1]))
         a = relay.nn.pad(a, pad_width=p)
         padding = (0, 0, 0, 0)
     else:
@@ -97,7 +97,7 @@ def test_conv2d(device, dtype):
     trials = [
         # Normal convolution
         [3, 3, (1, 1), (1, 1), (1, 1), 4, (14, 10, 10), (False, False, False)],
-        [2, 1, (2, 2), (1, 1), (1, 1), 7, (15, 16, 12), (False, False, True)],
+        [2, 1, (2, 2), (1, 1), (1, 1), 7, (15, 16, 12), (True, False, True)],
         [3, 3, (2, 1), (1, 1), (1, 1), 4, (14, 10, 10), (False, True, False)],
         [3, 3, (2, 1), (1, 1), (1, 1), 4, (14, 10, 10), (False, True, True)],
         # Normal convolution