[Relay][ConvertLayout] Support for qnn.conv2d_transpose

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

@@ -78,3 +78,51 @@ def convert_qnn_conv2d(attrs, inputs, tinfos, desired_layouts):
         return relay.qnn.op.conv2d(*inputs, **new_attrs)
 
     raise ValueError("Layout %s is not yet supported" % desired_data_layout)
+
+
+@reg.register_convert_op_layout("qnn.conv2d_transpose")
+def convert_qnn_conv2d_transpose(attrs, inputs, tinfos, desired_layouts):
+    """Convert Layout pass registration for QNN conv2d_transpose op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    tinfos : list of types
+        List of input and output types
+    desired_layouts : list of layout strings
+        List of layouts defining our desired
+        layout for the data and kernel inputs respectively.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The transformed expr
+    """
+    # pylint: disable=import-outside-toplevel
+    from tvm import relay
+
+    assert (
+        len(desired_layouts) == 2
+    ), "A desired layout is expected for both of qnn.conv2d_transpose's inputs"
+    desired_data_layout, desired_kernel_layout = map(str, desired_layouts)
+    assert desired_data_layout != "default", "Data layout cannot be default"
+
+    new_attrs = dict(attrs)
+    new_attrs["data_layout"] = desired_data_layout
+
+    if desired_kernel_layout != "default":
+        new_attrs["kernel_layout"] = desired_kernel_layout
+        return relay.qnn.op.conv2d_transpose(*inputs, **new_attrs)
+
+    # Handle default kernel layouts
+    if desired_data_layout == "NCHW":
+        new_attrs["kernel_layout"] = "OIHW"
+        return relay.qnn.op.conv2d_transpose(*inputs, **new_attrs)
+    if desired_data_layout == "NHWC":
+        new_attrs["kernel_layout"] = "HWIO"
+        return relay.qnn.op.conv2d_transpose(*inputs, **new_attrs)
+
+    raise ValueError("Layout %s is not yet supported" % desired_data_layout)

tests/python/relay/test_pass_convert_op_layout.py

@@ -1100,6 +1100,74 @@ def expected():
     assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
 
 
+def test_qnn_conv_transpose_requantize_convert_layout():
+    def before():
+        x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
+        weight = relay.var("weight", shape=(3, 3, 64, 64), dtype="int8")
+        y = relay.qnn.op.conv2d_transpose(
+            x,
+            weight,
+            relay.const(1, "int32"),
+            relay.const(1, "int32"),
+            relay.const(1, "float32"),
+            relay.const(1, "float32"),
+            channels=64,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NHWC",
+            kernel_layout="HWIO",
+            out_dtype="int32",
+        )
+        y = relay.qnn.op.requantize(
+            y,
+            relay.const(1, "float32"),
+            relay.const(1, "int32"),
+            relay.const(1, "float32"),
+            relay.const(1, "int32"),
+            out_dtype="int32",
+        )
+        y = relay.nn.relu(y)
+        y = relay.Function([x, weight], y)
+        return y
+
+    def expected():
+        x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
+        weight = relay.var("weight", shape=(3, 3, 64, 64), dtype="int8")
+        x = relay.layout_transform(x, "NHWC", "NCHW")
+        weight = relay.layout_transform(weight, "HWIO", "OIHW")
+        y = relay.qnn.op.conv2d_transpose(
+            x,
+            weight,
+            relay.const(1, "int32"),
+            relay.const(1, "int32"),
+            relay.const(1, "float32"),
+            relay.const(1, "float32"),
+            channels=64,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            out_dtype="int32",
+        )
+        y = relay.qnn.op.requantize(
+            y,
+            relay.const(1, "float32"),
+            relay.const(1, "int32"),
+            relay.const(1, "float32"),
+            relay.const(1, "int32"),
+            axis=1,
+            out_dtype="int32",
+        )
+        y = relay.nn.relu(y)
+        y = relay.layout_transform(y, "NCHW", "NHWC")
+        y = relay.Function(relay.analysis.free_vars(y), y)
+        return y
+
+    a = before()
+    a = run_opt_pass(a, transform.ConvertLayout({"qnn.conv2d_transpose": ["NCHW", "default"]}))
+    b = run_opt_pass(expected(), transform.InferType())
+
+    assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
+
+
 def test_conv_convert_kernel_layout():
     """Check that convolution kernel layout is correctly transformed."""