[ONNX][apache#8838] QLinearSigmoid contrib op and Bug Fix for Dequant…

…izeLinear (apache#9028) * [ONNX][apache#8838] QLinearSigmoid contrib op and Bug Fix for DequantizeLinear * [ONNX][apache#8838] QLinearSigmoid contrib op and Bug Fix for DequantizeLinear * [ONNX][apache#8838] QLinearSigmoid contrib op and Bug Fix for DequantizeLinear * [ONNX][apache#8838] QLinearSigmoid contrib op and Bug Fix for DequantizeLinear
ylc · Jan 13, 2022 · 2c69cae · 2c69cae
1 parent 0b5fece
commit 2c69cae
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diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
@@ -3254,6 +3254,8 @@ def _impl_v10(cls, inputs, attr, params):
     def _impl_v13(cls, inputs, attr, params):
         data, scale, zp = inputs
         axis = attr.get("axis", 1)
+        if len(infer_shape(data)) <= 1:
+            axis = 0
         return _qnn.op.dequantize(data, scale, _op.cast(zp, "int32"), axis)
 
 
@@ -3428,6 +3430,28 @@ def _impl_v10(cls, inputs, attr, params):
         return _qnn.op.quantize(out, y_scale, y_zero_point, out_dtype=dtype)
 
 
+class QLinearSigmoid(OnnxOpConverter):
+    """Operator converter for QLinearSigmoid from Microsoft onnxruntime contrib opset."""
+
+    @classmethod
+    def _impl_v10(cls, inputs, attr, params):
+        x = inputs[0]
+        x_scale = get_scalar(inputs[1], params)
+        x_zero_point = get_scalar(inputs[2], params, "int32")
+        y_scale = fold_constant(get_scalar(inputs[3], params))
+        y_zero_point = get_scalar(inputs[4], params, "int32")
+
+        dtype = infer_type(x).checked_type.dtype
+
+        ## Apparently, onnxruntime doesn't do this op in integer, they dequantize to fp32
+        ## and then requantize after:
+        ## https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/
+        ## providers/dml/DmlExecutionProvider/src/GraphTransformer.cpp#L245
+        x = _qnn.op.dequantize(x, x_scale, x_zero_point)
+        out = _op.sigmoid(x)
+        return _qnn.op.quantize(out, y_scale, y_zero_point, out_dtype=dtype)
+
+
 class QLinearConcat(OnnxOpConverter):
     """Operator converter for QLinearConcat from Microsoft onnxruntime contrib opset."""
 
@@ -4084,6 +4108,7 @@ def _get_convert_map(opset):
         "QLinearConcat": QLinearConcat.get_converter(opset),
         "QLinearAdd": QLinearAdd.get_converter(opset),
         "QLinearMul": QLinearMul.get_converter(opset),
+        "QLinearSigmoid": QLinearSigmoid.get_converter(opset),
         "ConvInteger": ConvInteger.get_converter(opset),
         "QLinearAveragePool": QLinearAveragePool.get_converter(opset),
         "QLinearGlobalAveragePool": QLinearGlobalAveragePool.get_converter(opset),

diff --git a/tests/python/frontend/onnx/test_forward.py b/tests/python/frontend/onnx/test_forward.py
@@ -5542,11 +5542,38 @@ def verify_qlinearmul(a_shape, b_shape, c_shape):
         model = helper.make_model(graph, producer_name="qlinearmul_test")
         quantize_and_verify_with_ort(model, input_names, [a_shape, b_shape], target, dev)
 
+    verify_qlinearmul([7], [7], [7])
     verify_qlinearmul([4, 2], [4, 2], [4, 2])
     verify_qlinearmul([4, 2], [2], [4, 2])
     verify_qlinearmul([5, 1, 7], [2, 7], [5, 2, 7])
 
 
+@tvm.testing.parametrize_targets
+def test_qlinearsigmoid(target, dev):
+    def verify_qlinearsigmoid(a_shape):
+
+        a_array = np.random.random(a_shape).astype("float32")
+
+        input_nodes = [helper.make_tensor_value_info("a", TensorProto.FLOAT, list(a_shape))]
+
+        input_values = [a_array]
+
+        node = helper.make_node("Sigmoid", ["a"], ["B"])
+        graph = helper.make_graph(
+            [node],
+            "qlinearsigmoid_test",
+            inputs=input_nodes,
+            outputs=[helper.make_tensor_value_info("B", TensorProto.FLOAT, list(a_shape))],
+        )
+        model = helper.make_model(graph, producer_name="qlinearsigmoid_test")
+        quantize_and_verify_with_ort(model, ["a"], [a_shape], target, dev)
+
+    verify_qlinearsigmoid([4, 2])
+    verify_qlinearsigmoid([5])
+    verify_qlinearsigmoid([3, 4, 5])
+    verify_qlinearsigmoid([])
+
+
 @tvm.testing.parametrize_targets
 def test_random_uniform(target, dev):
     def get_random_uniform(shape, dtype="float32", high=1.0, low=0.0, seed=None):