Add Range op to ONNX, make tvm arange op support negative steps (apac…

…he#6647)

python/tvm/relay/frontend/onnx.py

@@ -1893,6 +1893,19 @@ def _impl_v1(cls, inputs, attr, params):
         return _op.topk(inputs[0], inputs[1], axis=axis)
 
 
+class Range(OnnxOpConverter):
+    """Operator converter for Range"""
+
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        if len(inputs) != 3:
+            raise ValueError("Expect 3 input only")
+
+        return _op.arange(
+            inputs[0], inputs[1], inputs[2], dtype=infer_type(inputs[0]).checked_type.dtype
+        )
+
+
 class MaxRoiPool(OnnxOpConverter):
     """Operator converter for MaxRoiPool."""
 
@@ -2115,6 +2128,7 @@ def _get_convert_map(opset):
         "Or": Or.get_converter(opset),
         "Resize": Resize.get_converter(opset),
         "NonZero": NonZero.get_converter(opset),
+        "Range": Range.get_converter(opset),
     }
 
 

python/tvm/relay/op/_transform.py

@@ -123,7 +123,10 @@ def compute_scatter_add(attrs, inputs, output_type):
 @script
 def _arange_shape_func(start, stop, step):
     out = output_tensor((1,), "int64")
-    out[0] = int64(ceil_div((int64(stop[0]) - int64(start[0])), int64(step[0])))
+    if step[0] < 0:
+        out[0] = int64(ceil_div((int64(start[0]) - int64(stop[0])), int64(-step[0])))
+    else:
+        out[0] = int64(ceil_div((int64(stop[0]) - int64(start[0])), int64(step[0])))
     return out
 
 

tests/python/frontend/onnx/test_forward.py

@@ -410,6 +410,42 @@ def test_power():
     _test_power_iteration((2, 3), (1, 3))
 
 
+def verify_range(start, limit, delta, dtype):
+    dtype_map = {
+        "float32": TensorProto.FLOAT,
+        "int32": TensorProto.INT32,
+        "int64": TensorProto.INT64,
+    }
+    dtype_onnx = dtype_map[dtype]
+    y = helper.make_node("Range", ["start", "limit", "delta"], ["output"])
+    graph = helper.make_graph(
+        [y],
+        "range_test",
+        inputs=[
+            helper.make_tensor_value_info("start", dtype_onnx, []),
+            helper.make_tensor_value_info("limit", dtype_onnx, []),
+            helper.make_tensor_value_info("delta", dtype_onnx, []),
+        ],
+        outputs=[
+            helper.make_tensor_value_info(
+                "output", dtype_onnx, np.arange(start, limit, delta).shape
+            )
+        ],
+    )
+    model = helper.make_model(graph, producer_name="range_test")
+    inputs = [np.array(x).astype(dtype) for x in [start, limit, delta]]
+    verify_with_ort_with_inputs(model, inputs, use_vm=True)
+
+
+@tvm.testing.uses_gpu
+def test_range():
+    for t in ["float32", "int32", "int64"]:
+        verify_range(0, 10, 1, t)
+        verify_range(2, 8, 2, t)
+        verify_range(-3, 6, 4, t)
+        verify_range(-2, -7, -1, t)
+
+
 @tvm.testing.uses_gpu
 def test_squeeze():
     in_shape = (1, 3, 1, 3, 1, 1)