[QNN] Support CallNode inputs in qnn.concatenate (apache#5360)

* [QNN] Support CallNode inputs in qnn.concatenate

Currently, qnn.concatenate assumes that its 1st arg
(data) is a TupleNode. This may not necessarily be true
if the input is a CallNode which returns a value of
tuple-type. This patch handles the CallNode case by
inserting TupleGetItemNodes.

* Fix lint

* Add test

Change-Id: I40b55517b8b1dabbeca89337f80c0c8e62e34981

* Use isinstance

Change-Id: I731a231113c5214528373ef52b603a9f05ec502a

* isinstance fix

Change-Id: Ib3495532f6e4feb5aae3d3096cedd4dc4676cdb4

* Use elif/else if

Change-Id: Id8123ea2dd9ce3d8267609de7b5602bb84b084fb

* Fix lint

Change-Id: Ib6899bb22260575aa3f5d8b51b5d2a0277ee2b10

* Lint fix

Change-Id: I56cf1930315344e42d956818a6c68e80836ae786

* Spaces

Change-Id: I3edab192e32bafa9ffdc915315791c63279d85dc

python/tvm/relay/qnn/op/qnn.py

@@ -18,7 +18,7 @@
 """QNN dialect operators."""
 
 from __future__ import absolute_import as _abs
-from tvm.relay.expr import Tuple
+from tvm.relay.expr import Tuple, TupleWrapper
 from tvm.relay.op.nn.util import get_pad_tuple2d
 from . import _make
 
@@ -156,7 +156,7 @@ def concatenate(data,
 
     Parameters
     ----------
-    data : Union(List[relay.Expr], Tuple[relay.Expr])
+    data : Union(List[relay.Expr], Tuple[relay.Expr], TupleWrapper[relay.Expr])
         The list of quantized tensors.
 
     input_scales : List[relay.Expr]
@@ -180,15 +180,16 @@ def concatenate(data,
         The concatenated quantized tensor.
     """
 
-    data = list(data)
-    if not data:
-        raise ValueError("relay.concatenate requires data to be non-empty.")
+    if isinstance(data, (list, tuple)):
+        data = Tuple(data)
+    elif isinstance(data, TupleWrapper):
+        data = data.tuple_value
     if not isinstance(axis, int):
         raise ValueError("For now, we only support integer axis")
     input_scales = list(input_scales)
     input_zero_points = list(input_zero_points)
 
-    return _make.concatenate(Tuple(data),
+    return _make.concatenate(data,
                              Tuple(input_scales),
                              Tuple(input_zero_points),
                              output_scale,

src/relay/qnn/op/concatenate.cc

@@ -149,8 +149,16 @@ Expr ConcatenateQnnCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
   // If the output qnn params do not match the input qnn params, we can call requantize on the input
   // expr first, followed by a concatenate on the requantized input exprs.
 
-  auto tuple_data = data.as<TupleNode>();
-  CHECK(tuple_data != nullptr);
+  Array<Expr> tuple_exprs;
+  if (data->IsInstance<TupleNode>()) {
+    tuple_exprs = data.as<TupleNode>()->fields;
+  } else if (data->IsInstance<CallNode>()) {  // if the data is a CallNode, use TupleGetItems
+    auto call = Downcast<Call>(data);
+    for (size_t i = 0; i < tuple_type->fields.size(); i++) {
+      tuple_exprs.push_back(TupleGetItem(call, i));
+    }
+  }
+  CHECK(!tuple_exprs.empty());
 
   auto tuple_input_scales = input_scales.as<TupleNode>();
   CHECK(tuple_input_scales != nullptr);
@@ -160,7 +168,7 @@ Expr ConcatenateQnnCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
 
   int idx = 0;
   Array<Expr> requantized_exprs;
-  for (auto quantized_expr : tuple_data->fields) {
+  for (auto quantized_expr : tuple_exprs) {
     // Get the input scale for the idx quantized input tensor.
     auto input_scale = tuple_input_scales->fields[idx];
 

tests/python/relay/test_op_qnn_concatenate.py

@@ -144,7 +144,32 @@ def test_same_i_qnn_params():
     op_res = intrp.evaluate(func)(x_data, y_data)
     np.testing.assert_equal(op_res.asnumpy(), golden_output)
 
+def test_call_input():
+    # This tests the case where the input to concatenate is not explicitly a
+    # tuple node but is instead a call node.
+    x_data = np.ones(shape=(64,)).astype('uint8')
+
+    x = relay.var("x", shape=(64,), dtype='uint8')
+    x_scale = relay.const(1, 'float32')
+    y_scale = relay.const(1, 'float32')
+    x_zero_point = relay.const(0, 'int32')
+    y_zero_point = relay.const(0, 'int32')
+
+    tup = relay.split(x, 2, axis=0)
+    z = relay.qnn.op.concatenate(tup,
+                                 input_scales=(x_scale, y_scale),
+                                 input_zero_points=(x_zero_point, y_zero_point),
+                                 output_scale=y_scale,
+                                 output_zero_point=relay.const(0, 'int32'),
+                                 axis=0)
+    func = relay.Function([x], z)
+
+    intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
+    op_res = intrp.evaluate(func)(x_data)
+    np.testing.assert_equal(op_res.asnumpy(), x_data)
+
 if __name__ == '__main__':
+    test_call_input()
     test_same_io_qnn_params()
     test_different_io_qnn_params()
     test_few_same_io_qnn_params()