[FRONTEND][TFLITE][BugFix] Fix int16 transpose conv loading

python/tvm/relay/frontend/tflite.py

@@ -3300,6 +3300,7 @@ def convert_transpose_conv(self, op):
             kernel_zero_point = weights_tensor.qnn_params["zero_point"]
             input_scale = input_tensor.qnn_params["scale"]
             kernel_scale = weights_tensor.qnn_params["scale"]
+            out_dtype = "int64" if output_tensor_type_str == "int16" else "int32"
             out = _qnn.op.conv2d_transpose(
                 in_expr,
                 weight_expr_iohw,
@@ -3313,7 +3314,7 @@ def convert_transpose_conv(self, op):
                 kernel_size=(int(kernel_h), int(kernel_w)),
                 data_layout="NHWC",
                 kernel_layout="IOHW",
-                out_dtype="int32",
+                out_dtype=out_dtype,
             )
         else:
             out = _op.nn.conv2d_transpose(

src/relay/qnn/op/convolution_transpose.cc

@@ -99,7 +99,7 @@ bool QnnConv2DTransposeRel(const Array<Type>& types, int num_inputs, const Attrs
   ICHECK(weight->dtype == DataType::Int(8) || weight->dtype == DataType::UInt(8))
       << "Expected qnn conv2d type(int8, uint8) for weight but was " << weight->dtype;
   ICHECK(param->out_dtype == DataType::Int(16) || param->out_dtype == DataType::Int(32) ||
-         data->dtype == DataType::Int(64))
+         param->out_dtype == DataType::Int(64))
       << "Expected qnn conv2d type(int16, int32, int64) for output but was " << param->out_dtype;
   ICHECK(param->out_dtype.bits() > 0) << "Output dtype bits should be greater than 0.";
 

tests/python/frontend/tflite/test_forward.py

@@ -1648,6 +1648,86 @@ def test_forward_transpose_conv():
             )
 
 
+def _test_tflite2_quantized_transpose_conv(
+    input_shape,
+    kernel_shape,
+    filters,
+    padding="valid",
+    strides=(1, 1),
+    data_format=None,
+    int_quant_dtype=tf.int8,
+):
+    """One iteration of TFLite2 quantized tranpose conv with given shapes and attributes"""
+    data_format = "channels_last" if data_format == "NHWC" else "channels_first"
+    data = np.random.uniform(0, 1, input_shape).astype("float32")
+    _ = np.random.uniform(0, 1, kernel_shape).astype("float32")
+
+    data_in = tf.keras.layers.Input(shape=data.shape[1:], batch_size=1)
+    transpose_conv = tf.keras.layers.Conv2DTranspose(
+        filters=filters,
+        kernel_size=(kernel_shape[0], kernel_shape[1]),
+        padding=padding,
+        strides=strides,
+        use_bias=True,
+    )(data_in)
+    keras_model = tf.keras.models.Model(data_in, transpose_conv)
+
+    # To create quantized values with dynamic range of activations, needs representative dataset
+    def representative_data_gen():
+        for _ in range(1):
+            yield [data]
+
+    tflite_model_quant = _quantize_keras_model(
+        keras_model,
+        representative_data_gen,
+        is_float_input=True,
+        is_float_output=True,
+        int_quant_dtype=int_quant_dtype,
+    )
+
+    # TFLite.Model.Model has changed to TFLite.Model from 1.14 to 2.1
+    try:
+        import tflite.Model
+
+        tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_quant, 0)
+    except AttributeError:
+        import tflite
+
+        tflite_model = tflite.Model.GetRootAsModel(tflite_model_quant, 0)
+    except ImportError as exc:
+        raise ImportError("The tflite package must be installed") from exc
+
+    subgraph = tflite_model.Subgraphs(0)
+    model_input = subgraph.InputsAsNumpy()
+    input_node = subgraph.Tensors(model_input).Name().decode("utf-8")
+
+    tflite_output = run_tflite_graph(tflite_model_quant, data)
+
+    if tf.__version__ < LooseVersion("2.9"):
+        input_node = data_in.name.replace(":0", "")
+    else:
+        input_node = "serving_default_" + data_in.name + ":0"
+
+    tvm_output = run_tvm_graph(tflite_model_quant, data, input_node)
+    tvm.testing.assert_allclose(
+        np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-2, atol=1e-2
+    )
+
+
+def test_forward_quantized_transpose_conv():
+    """Quantized convolution"""
+    for int_quant_dtype in [tf.int8, tf.int16]:
+        _test_tflite2_quantized_transpose_conv(
+            (1, 1, 5, 64),
+            (3, 3),
+            64,
+            padding="same",
+            strides=(1, 2),
+            data_format="NHWC",
+            int_quant_dtype=int_quant_dtype,
+        )
+
+
 #######################################################################
 # Reshape
 # -------