[Frontend] Use f-strings for string formatting, NFC (apache#14830)

* [Frontend] Use f-strings for string formatting, NFC

Replace uses of % and .format() with f-strings.

* Format modified files

python/tvm/relay/frontend/caffe.py

@@ -133,7 +133,7 @@ def convert_eltwise(self, op):
                 out = _op.maximum(out, extra_expr)
         else:
             raise tvm.error.OpNotImplemented(
-                "eltwise_type {} is not supported for frontend Caffe.".format(eltwise_type)
+                f"eltwise_type {eltwise_type} is not supported for frontend Caffe."
             )
 
         return out
@@ -351,7 +351,7 @@ def convert_conv(self, op):
             weight_value = np.asarray(weight.data, np.float32)
             weight_value = np.reshape(weight_value, weight_shape)
         else:
-            raise Exception("No weight value of layer {} in caffemodel".format(op.name))
+            raise Exception(f"No weight value of layer {op.name} in caffemodel")
 
         weight_expr = self.exp_tab.new_const(weight_value, dtype="float32")
         in_expr = self.exp_tab.get_expr(inputs[0])
@@ -416,9 +416,7 @@ def convert_pooling(self, op):
                 out = _op.nn.avg_pool2d(in_expr, **params)
         else:
             raise tvm.error.OpNotImplemented(
-                "Operator {} is not supported for frontend Caffe.".format(
-                    pool_type_dict[pool_type] + " pool"
-                )
+                f"Operator {pool_type_dict[pool_type]} pool is not supported for frontend Caffe."
             )
 
         return out
@@ -465,7 +463,7 @@ def convert_innerproduct(self, op):
             weight_value = np.reshape(weight_value, (params["num_output"], -1))
             weight_shape = weight_value.shape
         else:
-            raise Exception("No weight value of layer {} in caffemodel".format(op.name))
+            raise Exception(f"No weight value of layer {op.name} in caffemodel")
 
         weight_expr = self.exp_tab.new_const(weight_value, dtype="float32")
 
@@ -548,9 +546,7 @@ def convert_deconv(self, op):
             # weight shape is in relay's IOHW format rn, we need it to be OIHW
             weight_value = np.transpose(weight_value, [1, 0, 2, 3])
         else:
-            raise tvm.error.OpAttributeRequired(
-                "No weight value of layer {} in caffemodel".format(op.name)
-            )
+            raise tvm.error.OpAttributeRequired(f"No weight value of layer {op.name} in caffemodel")
 
         weight_expr = self.exp_tab.new_const(weight_value, dtype="float32")
         in_expr = self.exp_tab.get_expr(inputs[0])
@@ -670,7 +666,7 @@ def convert_reduction(self, op):
             out = _op.sum(in_expr, axis=axis)
         else:
             raise tvm.error.OpAttributeInvalid(
-                "reduction method:{} is invalid in Caffe frontend.".format(method)
+                f"reduction method:{method} is invalid in Caffe frontend."
             )
 
         if float(coeff) != 1.0:

python/tvm/relay/frontend/caffe2.py

@@ -36,7 +36,7 @@ def _impl(attr):
         if len(kernel) == 2:
             return prefix + "2d" + surfix
         raise tvm.error.OpAttributeUnImplemented(
-            "Non-2D kernels are not supported for operator {}2d".format(prefix)
+            f"Non-2D kernels are not supported for operator {prefix}2d"
         )
 
     return _impl
@@ -122,7 +122,7 @@ def get_converter(cls):
         if hasattr(cls, "_impl"):
             return getattr(cls, "_impl")
         raise tvm.error.OpNotImplemented(
-            "Operator {} is not supported in frontend Caffe2.".format(cls.__name__)
+            f"Operator {cls.__name__} is not supported in frontend Caffe2."
         )
 
 
@@ -151,7 +151,7 @@ class Elemwise(Caffe2OpConverter):
 
     @classmethod
     def _impl(cls, inputs, args, params):
-        assert len(inputs) == 2, "Math op take 2 inputs, {} given".format(len(inputs))
+        assert len(inputs) == 2, f"Math op take 2 inputs, {len(inputs)} given"
         op_name = cls.name
         conv_ops = ["conv2d", "conv2d_transpose"]
         if args.get("broadcast", 0) and any(x in str(inputs[0]) for x in conv_ops):
@@ -282,14 +282,12 @@ def _get_axis_from_order_str(order):
             if order == "NHWC":
                 return 3
             raise tvm.error.OpAttributeUnImplemented(
-                "Order {} is not supported in operator Concat.".format(order)
+                f"Order {order} is not supported in operator Concat."
             )
 
         return AttrCvt(
             op_name="concatenate",
-            transforms={
-                "order": ("axis", (1), _get_axis_from_order_str),
-            },
+            transforms={"order": ("axis", (1), _get_axis_from_order_str)},
             excludes=["add_axis"],
         )((inputs,), args, params)
 
@@ -498,9 +496,7 @@ def _get_node(self, blob):
         if blob in self._nodes:
             return self._nodes[blob]
 
-        assert blob not in self._visited_nodes, "Cyclic dependency in the graph (in {})".format(
-            blob
-        )
+        assert blob not in self._visited_nodes, f"Cyclic dependency in the graph (in {blob})"
         self._visited_nodes.add(blob)
 
         self._process_op(self._ops[blob])
@@ -531,12 +527,12 @@ def _parse_arg(self, arg):
                     args[a.name] = tuple(getattr(a, f))
             for f in ["n"]:
                 if a.HasField(f):
-                    raise NotImplementedError("Field {} is not supported in relay.".format(f))
+                    raise NotImplementedError(f"Field {f} is not supported in relay.")
             for f in ["nets"]:
                 if list(getattr(a, f)):
-                    raise NotImplementedError("Field {} is not supported in relay.".format(f))
+                    raise NotImplementedError(f"Field {f} is not supported in relay.")
             if a.name not in args:
-                raise ValueError("Cannot parse attribute: \n{}\n.".format(a))
+                raise ValueError(f"Cannot parse attribute: \n{a}\n.")
         return args
 
     def _convert_operator(self, op_type, inputs, args, identity_list=None, convert_map=None):
@@ -573,7 +569,7 @@ def _convert_operator(self, op_type, inputs, args, identity_list=None, convert_m
             func = convert_map[op_type](inputs, args, self._params)
         else:
             raise tvm.error.OpNotImplemented(
-                "Operator {} is not supported in frontend Caffe2.".format(op_type)
+                f"Operator {op_type} is not supported in frontend Caffe2."
             )
         return func
 

python/tvm/relay/frontend/common.py

@@ -99,7 +99,7 @@ def get_float(self, key, default=RequiredAttr()):
         if key in self.attrs:
             return float(self.attrs[key])
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
     def get_int(self, key, default=RequiredAttr()):
@@ -123,7 +123,7 @@ def get_int(self, key, default=RequiredAttr()):
                 return None
             return int(val)
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
     def get_str(self, key, default=RequiredAttr()):
@@ -144,7 +144,7 @@ def get_str(self, key, default=RequiredAttr()):
         if key in self.attrs:
             return self.attrs[key]
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
     def get_int_tuple(self, key, default=RequiredAttr()):
@@ -170,7 +170,7 @@ def get_int_tuple(self, key, default=RequiredAttr()):
                 if x
             )
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
     def get_float_tuple(self, key, default=RequiredAttr()):
@@ -193,7 +193,7 @@ def get_float_tuple(self, key, default=RequiredAttr()):
             tshape = self.attrs[key]
             return tuple(float(x.strip()) for x in tshape.strip("()[]").split(","))
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
     def get_tuple_tuple_int(self, key, default=RequiredAttr()):
@@ -222,7 +222,7 @@ def get_tuple_tuple_int(self, key, default=RequiredAttr()):
             return tuple(seq)
 
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
     def get_int_list(self, key, default=RequiredAttr()):
@@ -244,7 +244,7 @@ def get_int_list(self, key, default=RequiredAttr()):
             tshape = self.attrs[key]
             return tuple(int(x.strip()) for x in tshape.strip("[]()").split(","))
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
     def get_bool(self, key, default=RequiredAttr()):
@@ -266,7 +266,7 @@ def get_bool(self, key, default=RequiredAttr()):
             val = self.attrs[key]
             return val.strip().lower() in ["true", "1", "t", "y", "yes"]
         if isinstance(default, RequiredAttr):
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return default
 
 
@@ -292,7 +292,7 @@ def get_relay_op(op_name):
             if op is not None:
                 break
     if not op:
-        raise tvm.error.OpNotImplemented("Unable to map op_name {} to relay".format(op_name))
+        raise tvm.error.OpNotImplemented(f"Unable to map op_name {op_name} to relay")
     return op
 
 
@@ -307,7 +307,7 @@ def __init__(self):
 
     def new_const(self, value, shape=None, dtype="float32", source_name=None):
         """Construct a new var expr and add to exprs dictionary"""
-        name = "_param_%d" % (self.const_ctr)
+        name = f"_param_{self.const_ctr}"
         if hasattr(value, "shape"):
             shape = value.shape
         self.const_ctr += 1
@@ -412,7 +412,7 @@ def __call__(self, inputs, attrs, *args):
         if self._custom_check:
             func, msg = self._custom_check
             if not func(attrs):
-                raise RuntimeError("Check failed: {}".format(msg))
+                raise RuntimeError(f"Check failed: {msg}")
         # get new op_name
         if isinstance(self._op_name, str):
             op_name = self._op_name
@@ -465,7 +465,7 @@ def _parse_default(self, target):
         else:
             k = None  # should raise
         if not isinstance(k, str):
-            msg = "{} is not a valid target, (name, default) expected.".format(target)
+            msg = f"{target} is not a valid target, (name, default) expected."
             raise ValueError(msg)
         return k, v, t
 
@@ -479,7 +479,7 @@ def _required_attr(self, attr, key):
         """Wrapper for getting required attributes."""
         assert isinstance(attr, dict)
         if key not in attr:
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise AttributeError(f"Required attribute {key} not found.")
         return attr[key]
 
 
@@ -1035,7 +1035,7 @@ def ensure_scalar_shape(x):
         return x
 
     num_elem = np.prod(x_shape)
-    assert num_elem == 1, "Cannot squeeze tensor shape {} to scalar form.".format(x_shape)
+    assert num_elem == 1, f"Cannot squeeze tensor shape {x_shape} to scalar form."
 
     return _op.squeeze(x)
 

python/tvm/relay/frontend/coreml.py

@@ -183,7 +183,7 @@ def _ActivationParams(op, inexpr, etab):
         beta_expr = etab.new_const(beta)
         return _op.multiply(_op.log(_op.add(_op.exp(inexpr), beta_expr)), alpha_expr)
     raise tvm.error.OpNotImplemented(
-        "Operator {} is not supported in frontend CoreML.".format(whichActivation)
+        f"Operator {whichActivation} is not supported in frontend CoreML."
     )
 
 
@@ -231,9 +231,9 @@ def _PoolingLayerParams(op, inexpr, etab):
         params["padding"] = padding
         params["ceil_mode"] = True
     else:
-        msg = "PoolingPaddingType {} is not supported in operator Pooling."
         op_name = op.WhichOneof("PoolingPaddingType")
-        raise tvm.error.OpAttributeUnImplemented(msg.format(op_name))
+        msg = f"PoolingPaddingType {op_name} is not supported in operator Pooling."
+        raise tvm.error.OpAttributeUnImplemented(msg)
 
     if op.type == 0:
         return _op.nn.max_pool2d(inexpr, **params)
@@ -302,7 +302,7 @@ def _PaddingLayerParams(op, inexpr, etab):
         constant = op.constant
         if constant.value != 0:
             raise tvm.error.OpAttributeUnImplemented(
-                "{} is not supported in operator Padding.".format(constant.value)
+                f"{constant.value} is not supported in operator Padding."
             )
         pad_t = op.paddingAmounts.borderAmounts[0].startEdgeSize
         pad_l = op.paddingAmounts.borderAmounts[1].startEdgeSize
@@ -391,8 +391,8 @@ def _UnaryFunctionLayerParams(op, inexpr, etab):
         alpha = _expr.const(op.alpha)
         return _op.maximum(inexpr, alpha)
     else:
-        msg = "Unary Op type value {} is not supported in frontend CoreML."
-        raise tvm.error.OpAttributeUnImplemented(msg.format(op_type))
+        msg = f"Unary Op type value {op_type} is not supported in frontend CoreML."
+        raise tvm.error.OpAttributeUnImplemented(msg)
 
 
 def _ReduceLayerParams(op, inexpr, etab):
@@ -408,8 +408,8 @@ def _ReduceLayerParams(op, inexpr, etab):
     elif axis == op.W:
         axis = -1
     else:
-        msg = "Reduce axis value {} is not supported in frontend CoreML."
-        raise tvm.error.OpAttributeUnImplemented(msg.format(axis))
+        msg = f"Reduce axis value {axis} is not supported in frontend CoreML."
+        raise tvm.error.OpAttributeUnImplemented(msg)
 
     mode = op.mode
     if mode == op.SUM:
@@ -425,8 +425,8 @@ def _ReduceLayerParams(op, inexpr, etab):
     elif mode == op.ARGMAX:
         return _op.argmax(inexpr, axis=axis, keepdims=True)
     else:
-        msg = "Reduce mode value {} is not supported in frontend CoreML."
-        raise tvm.error.OpAttributeUnImplemented(msg.format(mode))
+        msg = f"Reduce mode value {mode} is not supported in frontend CoreML."
+        raise tvm.error.OpAttributeUnImplemented(msg)
 
 
 def _ReshapeLayerParams(op, inexpr, etab):
@@ -511,7 +511,7 @@ def coreml_op_to_relay(op, inname, outnames, etab):
     classname = type(op).__name__
     if classname not in _convert_map:
         raise tvm.error.OpNotImplemented(
-            "Operator {} is not supported in frontend CoreML.".format(classname)
+            f"Operator {classname} is not supported in frontend CoreML."
         )
     if isinstance(inname, _base.string_types):
         insym = etab.get_expr(inname)

python/tvm/relay/frontend/darknet.py

@@ -34,8 +34,7 @@
 
 def _darknet_not_support(attr, op="relay"):
     """Raise error if any operation is not supported."""
-    err = "{} is not supported in {}.".format(attr, op)
-    raise NotImplementedError(err)
+    raise NotImplementedError(f"{attr} is not supported in {op}.")
 
 
 def _get_params_prefix(opname, layer_num):
@@ -51,7 +50,7 @@ def _get_params_name(prefix, item):
 def _get_param_var(params, prefix, item):
     name = _get_params_name(prefix, item)
     if name not in params:
-        raise AttributeError("{} not found in params dict.".format(name))
+        raise AttributeError(f"{name} not found in params dict.")
     return new_var(name, shape=params[name].shape, dtype=params[name].dtype)
 
 
@@ -688,7 +687,7 @@ def _get_darknet_attrs(self, layer, layer_num):
             pass
 
         else:
-            err = "Darknet layer type {} is not supported in relay.".format(layer_type)
+            err = f"Darknet layer type {layer_type} is not supported in relay."
             raise NotImplementedError(err)
 
         return attr
@@ -743,7 +742,7 @@ def _get_opname(self, layer):
 
     def _new_rnn_state_var(self, state=None, name="rnn"):
         """Returs a symbol for state"""
-        sym_name = name + "%d_state" % self._state_ctr[name]
+        sym_name = name + f"{self._state_ctr[name]}_state"
         self._state_ctr[name] += 1
         return new_var(sym_name, shape=state.shape, dtype=str(state.dtype))