Fix InferCorrectLayout for dynamic upsampling and add a regression test

src/relay/op/dyn/nn/upsampling.cc

@@ -22,13 +22,14 @@
  * \brief upsampling operator
  */
 
-#include "../../nn/upsampling.h"
+#include "upsampling.h"
 
 #include <tvm/relay/attrs/nn.h>
 #include <tvm/relay/op.h>
 #include <tvm/relay/op_attr_types.h>
 #include <tvm/tir/data_layout.h>
 
+#include <utility>
 #include <vector>
 
 #include "../../op_common.h"
@@ -48,7 +49,6 @@ bool UpSamplingRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   if (scale_h == nullptr) return false;
   if (scale_w == nullptr) return false;
 
-  CHECK_EQ(data->shape.size(), 4);
   CHECK_EQ(scale_h->shape.size(), 0);
   CHECK_EQ(scale_w->shape.size(), 0);
   static const Layout kNCHW("NCHW");

src/relay/op/dyn/nn/upsampling.h

@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *
+ * \file src/relay/op/dyn/nn/upsampling.h
+ * \brief implementation of the InferCorrectLayout pass for dynamic upsampling
+ */
+
+#ifndef TVM_RELAY_OP_DYN_NN_UPSAMPLING_H_
+#define TVM_RELAY_OP_DYN_NN_UPSAMPLING_H_
+
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/tir/data_layout.h>
+
+#include "../../op_common.h"
+
+namespace tvm {
+namespace relay {
+namespace dyn {
+
+template <typename T>
+Array<Array<Layout> > UpsamplingInferCorrectLayout(const Attrs& attrs,
+                                                   const Array<Layout>& new_in_layouts,
+                                                   const Array<Layout>& old_in_layouts,
+                                                   const Array<tvm::relay::Type>& old_in_types) {
+  // NOTE: Discard "const" qualifier here.
+  T* params = const_cast<T*>(attrs.as<T>());
+  if (new_in_layouts.defined()) {
+    CHECK_GT(new_in_layouts.size(), 0);
+
+    Layout raw_layout(params->layout);
+    Layout input = new_in_layouts[0];
+    if (input.IndexOf(LayoutAxis::Get('W')) == raw_layout.IndexOf(LayoutAxis::Get('W')) &&
+        input.IndexOf(LayoutAxis::Get('H')) == raw_layout.IndexOf(LayoutAxis::Get('H')) &&
+        !input.Contains(LayoutAxis::Get('w')) && !input.Contains(LayoutAxis::Get('h')) &&
+        (input.IndexOf(LayoutAxis::Get('D')) == -1 ||
+         (input.IndexOf(LayoutAxis::Get('D')) == raw_layout.IndexOf(LayoutAxis::Get('D')) &&
+          !input.Contains(LayoutAxis::Get('d'))))) {
+      params->layout = input.name();  // modify self to follow the input layout
+    }
+  }
+
+  Layout inferred_layout(params->layout);
+  Layout param_layout("NCHW");
+  return Array<Array<Layout> >{{inferred_layout, param_layout, param_layout}, {inferred_layout}};
+}
+
+}  // namespace dyn
+}  // namespace relay
+}  // namespace tvm
+
+#endif  // TVM_RELAY_OP_DYN_NN_UPSAMPLING_H_

tests/python/relay/test_pass_alter_op_layout.py

@@ -673,6 +673,45 @@ def expected():
     assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
 
 
+def test_alter_layout_nchw_dyn_upsamping_op():
+    """Test upsamping operators """
+
+    def before():
+        x = relay.var("x", shape=(1, 32, 28, 28))
+        weight = relay.var("weight", shape=(32, 32, 3, 3))
+        y = relay.nn.conv2d(x, weight, channels=32, kernel_size=(3, 3), padding=(1, 1))
+        y = relay.nn.upsampling(y, scale_h=relay.const(2), scale_w=relay.const(2))
+        y = relay.nn.avg_pool2d(y, pool_size=(2, 2), strides=(2, 2))
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    def alter_conv2d(attrs, inputs, tinfos, out_type):
+        data, weight = inputs
+        new_attrs = dict(attrs)
+        new_attrs["data_layout"] = "NCHW16c"
+        return relay.nn.conv2d(data, weight, **new_attrs)
+
+    def expected():
+        x = relay.var("x", shape=(1, 32, 28, 28))
+        weight = relay.var("weight")
+        x = relay.layout_transform(x, "NCHW", "NCHW16c")
+        y = relay.nn.conv2d(
+            x, weight, channels=32, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
+        )
+        y = relay.nn.upsampling(y, scale_h=relay.const(2), scale_w=relay.const(2), layout="NCHW16c")
+        y = relay.nn.avg_pool2d(y, pool_size=(2, 2), strides=(2, 2), layout="NCHW16c")
+        y = relay.layout_transform(y, "NCHW16c", "NCHW")
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
+        a = before()
+        a = run_opt_pass(a, transform.AlterOpLayout())
+        b = run_opt_pass(expected(), transform.InferType())
+
+    assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
+
+
 @tvm.testing.uses_gpu
 def test_alter_layout_strided_slice():
     """Test rewriting strided_slice during alter_iop_layout"""