Add FlattenAtrousConv pass into the default optimize pipeline.

include/tvm/relay/transform.h

@@ -494,6 +494,15 @@ TVM_DLL Pass ManifestLifetimes();
  */
 TVM_DLL Pass PlanDevices(CompilationConfig config);
 
+/*!
+ * \brief This transform flattens atrous convolution, which corresponds to the sequence of
+ * operations: "space_to_batch_nd"->"conv2d"->"batch_to_space_nd" and convert them into subgraphs
+ * with a convolution with the modified "dilation" and recalculated "padding" parameters.
+ *
+ * \return The pass.
+ */
+TVM_DLL Pass FlattenAtrousConv();
+
 }  // namespace transform
 
 /*!

src/relay/backend/utils.cc

@@ -262,6 +262,8 @@ Array<Pass> GetPassPrefix(bool is_homegeneous, bool is_vm) {
   // Fast math optimizations.
   pass_seqs.push_back(transform::FastMath());
   pass_seqs.push_back(transform::FoldConstant());
+
+  pass_seqs.push_back(transform::FlattenAtrousConv());
   return pass_seqs;
 }
 

tests/python/relay/test_pass_flatten_atrous_conv.py

@@ -19,6 +19,7 @@
 import pytest
 import tvm
 from tvm import relay
+from tvm.contrib import graph_executor
 
 
 def compare_expected_fac(expr, expected_expr, args):
@@ -421,6 +422,50 @@ def test_fac_op_btwn_conv_b2s():
     compare_expected_fac(expr, expected_expr, [x_np])
 
 
+def test_fac_relay_build():
+    #  Check the default optimize pipeline
+    shape_x = [1, 5, 5, 4]
+    shape_w = [3, 3, 4, 1]
+
+    x_np = np.random.randint(-128, 127, size=shape_x, dtype="int8").astype("float32")
+    w_np = np.random.randint(-128, 127, size=shape_w, dtype="int8").astype("float32")
+
+    weight = relay.const(w_np)
+    data = relay.var("data", shape=shape_x, dtype="float32")
+    op1 = relay.nn.space_to_batch_nd(data, block_shape=[2, 2], paddings=[[2, 3], [2, 3]])
+    op2 = relay.nn.conv2d(
+        op1,
+        weight,
+        padding=[0, 0, 0, 0],
+        groups=4,
+        channels=4,
+        kernel_size=[3, 3],
+        data_layout="NHWC",
+        kernel_layout="HWOI",
+    )
+    expr = relay.nn.batch_to_space_nd(op2, block_shape=[2, 2], crops=[[0, 1], [0, 1]])
+
+    mod_def = tvm.relay.transform.InferType()(tvm.IRModule.from_expr(expr))
+    result_def = (
+        relay.create_executor("vm", mod=mod_def, device=tvm.cpu(), target="llvm")
+        .evaluate()(x_np)
+        .numpy()
+    )
+
+    graph, lib, params = relay.build(mod_def, "llvm", params=None)
+    rt_mod = graph_executor.create(graph, lib, device=tvm.cpu())
+    rt_mod.set_input("data", x_np)
+    rt_mod.set_input(**params)
+    rt_mod.run()
+    result_flat = rt_mod.get_output(0).numpy()
+
+    assert "space_to_batch_nd" not in graph
+    assert "conv2d" in graph
+    assert "batch_to_space_nd" not in graph
+
+    assert np.array_equal(result_def, result_flat)
+
+
 if __name__ == "__main__":
     import sys