lint

python/tvm/relay/op/_tensor.py

@@ -88,7 +88,8 @@
 register_broadcast_schedule("fast_tanh")
 register_broadcast_schedule("fast_erf")
 # a fake on_device schedule.
-# this will not be used in actual computation as on_device will be removed during DeviceAnnotation pass
+# this will not be used in actual computation
+# as on_device will be removed during DeviceAnnotation pass
 register_injective_schedule("on_device")
 
 

src/relay/op/annotation/annotation.cc

@@ -56,10 +56,9 @@ RELAY_REGISTER_OP("on_device")
     .set_attr<TOpIsStateful>("TOpIsStateful", false)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout)
     .set_attr<FTVMCompute>("FTVMCompute",
-                           [] (const Attrs& attrs,
-                               const Array<te::Tensor>& inputs,
-                               const Type& out_type) -> Array<te::Tensor> {
-                           return {topi::identity(inputs[0])};
+                           [](const Attrs& attrs, const Array<te::Tensor>& inputs,
+                              const Type& out_type) -> Array<te::Tensor> {
+                             return {topi::identity(inputs[0])};
                            });
 
 Expr StopFusion(Expr data) {

vta/python/vta/top/graphpack.py

@@ -201,21 +201,20 @@ def __init__(self, start=-1, end=-1):
     def visit_call(self, call):
         """ Visit the children. """
         # First visit the children.
-        oshape = _get_tensor_shape(call)
-        odtype = _get_tensor_type(call)
-        input_types = [arg.checked_type for arg in call.args]
         args = [self.visit(arg) for arg in call.args]
 
         self.counter += 1
         if self.counter == self.start:
             ret = relay.Call(call.op, args, call.attrs)
             ret = relay.annotation.on_device(ret, self.ext_ctx)
             return ret
-        elif self.counter == self.end:
+
+        if self.counter == self.end:
             ret = relay.Call(call.op, args, call.attrs)
             ret = relay.annotation.on_device(ret, self.cpu_ctx)
             return ret
-        elif self.counter > self.start and self.counter < self.end:
+
+        if self.counter > self.start and self.counter < self.end:
             ret = relay.Call(call.op, args, call.attrs)
 
             # skip the float op, i.e., float->int cast
@@ -234,11 +233,11 @@ def is_float_op(self, call):
         """
         args = call.args
         odtype = _get_tensor_type(call)
-        op = call.op
 
         if odtype == "float32":
             return True
-        elif op == self.cast:
+
+        if call.op == self.cast:
             idtype = _get_tensor_type(args[0])
             if idtype == "float32":
                 return True
@@ -566,7 +565,8 @@ def graph_pack(expr,
     """
     assert isinstance(expr, relay.Function)
     assert ((start_name != stop_name) or (start_name_idx is None != stop_name_idx is None) or \
-            (not (start_name_idx is None and stop_name_idx is None)) or (start_name_idx < stop_name_idx))
+            (not (start_name_idx is None and stop_name_idx is None)) \
+            or (start_name_idx < stop_name_idx))
     expr = get_subgraph(expr, start_name, stop_name, start_name_idx, stop_name_idx, count_meta)
     expr = run_opt_pass(expr, transform.InferType())
     packer = ExprPack(
@@ -589,8 +589,6 @@ def graph_pack(expr,
 
         device_annot = ExprDeviceAnnot(start=start, end=end)
         expr = device_annot.visit(expr)
-        ret = run_opt_pass(expr, transform.InferType())
+        return run_opt_pass(expr, transform.InferType())
 
-        return ret
-    else:
-        return expr
+    return expr