lint fixes

python/tvm/relay/frontend/onnx.py

@@ -2873,11 +2873,13 @@ def _inputs_check(cls, inputs):
         ), "Updates rank should be equal to data_rank + indices_rank - indices_shape[-1] - 1"
 
     @classmethod
-    def _reduction_check(cls, attr, red_valids=["update"]):
+    def _reduction_check(cls, attr, red_valids=None):
         reduction = attr.get("reduction", None)
         if reduction is None:
             reduction = b"update"
         reduction = reduction.decode("utf-8")
+        if red_valids is None:
+            red_valids = ["update"]
         assert reduction in red_valids, "Only {} reductions are supported, but {} is gotten".format(
             red_valids, reduction
         )
@@ -2889,9 +2891,7 @@ def _impl_v11(cls, inputs, attr, params):
         cls._inputs_check(inputs)
         indices_dim = len(infer_shape(inputs[1]))
         axes = list(range(indices_dim))
-        return _op.scatter_nd(
-            inputs[0], _op.transpose(inputs[1], axes[-1:] + axes[:-1]), inputs[2]
-        )
+        return _op.scatter_nd(inputs[0], _op.transpose(inputs[1], axes[-1:] + axes[:-1]), inputs[2])
 
     @classmethod
     def _impl_v16(cls, inputs, attr, params):

python/tvm/topi/cuda/scatter.py

@@ -874,9 +874,13 @@ def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr):
                         elif mode == "mul":
                             out[index] *= updates[i * fused_updates_dimension + j]
                         elif mode == "min":
-                            out[index] = tir.min(out[index], updates[i * fused_updates_dimension + j])
+                            out[index] = tir.min(
+                                out[index], updates[i * fused_updates_dimension + j]
+                            )
                         elif mode == "max":
-                            out[index] = tir.max(out[index], updates[i * fused_updates_dimension + j])
+                            out[index] = tir.max(
+                                out[index], updates[i * fused_updates_dimension + j]
+                            )
                         else:
                             raise NotImplementedError(
                                 "scatter_nd mode not in [update, add, mul, min, max]:", mode

python/tvm/topi/scatter.py

@@ -16,11 +16,11 @@
 # under the License.
 # pylint: disable=invalid-name, too-many-arguments, too-many-nested-blocks
 """Scatter operator"""
-from ..te import extern, hybrid
-from ..tir import decl_buffer, expr, ir_builder, min, max
+from tvm import te, tir  # hide redefinition of min and max
+from tvm.tir import expr
 
 
-@hybrid.script
+@te.hybrid.script
 def _scatter_1d(data, indices, updates):
     out = output_tensor(data.shape, data.dtype)
     for i in range(data.shape[0]):
@@ -30,7 +30,7 @@ def _scatter_1d(data, indices, updates):
     return out
 
 
-@hybrid.script
+@te.hybrid.script
 def _scatter_2d(data, indices, updates, axis):
     out = output_tensor(data.shape, data.dtype)
     for i in range(data.shape[0]):
@@ -52,7 +52,7 @@ def _scatter_2d(data, indices, updates, axis):
     return out
 
 
-@hybrid.script
+@te.hybrid.script
 def _scatter_3d(data, indices, updates, axis):
     out = output_tensor(data.shape, data.dtype)
     for i in range(data.shape[0]):
@@ -96,7 +96,7 @@ def _scatter_3d(data, indices, updates, axis):
     return out
 
 
-@hybrid.script
+@te.hybrid.script
 def _scatter_4d(data, indices, updates, axis):
     out = output_tensor(data.shape, data.dtype)
     for i in range(data.shape[0]):
@@ -269,7 +269,7 @@ def scatter_nd(data, indices, updates, mode):
 
     def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr):
         # pylint: disable=invalid-name
-        ib = ir_builder.create()
+        ib = tir.ir_builder.create()
 
         data = ib.buffer_ptr(data_ptr)
         indices = ib.buffer_ptr(indices_ptr)
@@ -311,18 +311,18 @@ def gen_ir(data_ptr, indices_ptr, updates_ptr, out_ptr):
                 elif mode == "mul":
                     out[index] *= updates[i * fused_updates_dimension + j]
                 elif mode == "min":
-                    out[index] = min(out[index], updates[i * fused_updates_dimension + j])
+                    out[index] = tir.min(out[index], updates[i * fused_updates_dimension + j])
                 elif mode == "max":
-                    out[index] = max(out[index], updates[i * fused_updates_dimension + j])
+                    out[index] = tir.max(out[index], updates[i * fused_updates_dimension + j])
                 else:
                     raise NotImplementedError(
                         "scatter_nd mode not in [update, add, mul, min, max]:", mode
                     )
 
         return ib.get()
 
-    out_buf = decl_buffer(data.shape, data.dtype, "out_buf")
-    return extern(
+    out_buf = tir.decl_buffer(data.shape, data.dtype, "out_buf")
+    return te.extern(
         [data.shape],
         [data, indices, updates],
         lambda ins, outs: gen_ir(ins[0], ins[1], ins[2], outs[0]),