[Dy2St] Fix typo runable -> runnable (#62779)

python/paddle/jit/dy2static/pir_partial_program.py

@@ -113,7 +113,7 @@ def iter_elements(self):
         yield from self.father.keys()
 
 
-class RunableProgram:
+class RunnableProgram:
     """a pir program ready for run_program_op to run. constructed by 3 parts:
     - pir program (pir::Program)
     - in_out_values
@@ -240,7 +240,7 @@ def clone(self):
         cloned_program, _ = paddle.base.libpaddle.pir.clone_program(
             self.program
         )
-        return RunableProgram(
+        return RunnableProgram(
             cloned_program,
             (self.x_names, self.param_names, self.out_names),
             None,
@@ -462,7 +462,7 @@ def __init__(
 
         # program_id -> list(scope)
         self._scope_cache = {}
-        self._hooker = []
+        self._hookers = []
         self._backend = kwargs.get('backend', None)
         self._grad_var_names = {}
         self._debug_name = None
@@ -506,7 +506,7 @@ def sot_call(self, inputs):
         return out_vars
 
     @cached_property
-    def origin_runable_program(self):
+    def origin_runnable_program(self):
         inputs = list(self._inputs.var_list)
         outputs = list(self._outputs.var_list)
         params = self._param_values
@@ -516,7 +516,7 @@ def origin_runable_program(self):
             len(self._origin_main_program.global_block().ops),
             "output_",
         )
-        return RunableProgram(
+        return RunnableProgram(
             self._origin_main_program, (inputs, params, outputs)
         )
 
@@ -536,7 +536,7 @@ def _sync_lr_value_with_scheduler(self):
             lr_var.set_value(data)
 
     def add_hooker(self, hooker):
-        self._hooker.append(hooker)
+        self._hookers.append(hooker)
 
     def _get_scope(self, program_id=None, use_scope_cache=False):
         if not use_scope_cache:
@@ -571,13 +571,15 @@ def pass_fn(forward_program, backward_program):
                 return forward_program, backward_program
 
             # TODO(xiongkun) who to transfer the pruning program?
-            infer_program = self.origin_runable_program.clone()
-            for hooker in self._hooker:
+            infer_program = self.origin_runnable_program.clone()
+            for hooker in self._hookers:
                 hooker.after_infer(infer_program)
             infer_program.apply_pir_program_pass(pass_fn)
             return infer_program
         else:
-            train_program: RunableProgram = self.origin_runable_program.clone()
+            train_program: RunnableProgram = (
+                self.origin_runnable_program.clone()
+            )
             train_program = self._append_backward_desc(train_program)
             # Note: Only set grad type once after initializing train program. So we put it here.
             self._set_grad_type(self._params, train_program)
@@ -722,11 +724,11 @@ def _insert_aggregation_ops_for_var(target_program, var):
             _insert_aggregation_ops_for_var(target_program, _var)
 
     @switch_to_static_graph
-    def _append_backward_desc(self, train_runnable_program: RunableProgram):
+    def _append_backward_desc(self, train_runnable_program: RunnableProgram):
         program = train_runnable_program.program
         targets = train_runnable_program.out_values
-        # TODO(@zhuoge): refine the interface, use runable_program to apply passes.
-        for hooker in self._hooker:
+        # TODO(@zhuoge): refine the interface, use runnable_program to apply passes.
+        for hooker in self._hookers:
             program, targets = hooker.before_append_backward(program, targets)
         inputs = train_runnable_program.x_values
         params = train_runnable_program.param_values
@@ -793,7 +795,7 @@ def _append_backward_desc(self, train_runnable_program: RunableProgram):
                                 forward_end_idx = idx + 1
                                 break
 
-            for hooker in self._hooker:
+            for hooker in self._hookers:
                 (
                     program,
                     forward_end_idx,
@@ -817,7 +819,7 @@ def _append_backward_desc(self, train_runnable_program: RunableProgram):
         p_grad_value = list(map(mapping_value, grad_info_map[inputs_size:]))
         o_grad_value = list(map(mapping_value, forward_outputs_grads))
 
-        # insert grads name for RunableProgram (we need name for grad_inputs and grad_outputs)
+        # insert grads name for RunnableProgram (we need name for grad_inputs and grad_outputs)
         input_grads_to_append = list(
             filter(lambda x: not is_fake_value(x), o_grad_value)
         )
@@ -836,7 +838,7 @@ def _append_backward_desc(self, train_runnable_program: RunableProgram):
             forward_end_idx + op_between_forward_and_backward
         )
         # construct a runnable program.
-        return RunableProgram(
+        return RunnableProgram(
             program,
             (inputs, params, targets),
             (x_grad_value, p_grad_value, o_grad_value),
@@ -1005,7 +1007,7 @@ def _remove_no_value(self, out_vars):
 
         return out_vars
 
-    def _set_grad_type(self, params, train_program: RunableProgram):
+    def _set_grad_type(self, params, train_program: RunnableProgram):
         # NOTE: if user set sparse gradient mode, the param's gradient
         # will be SelectedRows, not LoDTensor. But tracer will just
         # set param grad Tensor by forward Tensor(LoDTensor)

test/legacy_test/test_network_with_dtype.py

@@ -49,7 +49,7 @@ def run_net_on_place(self, place):
         exe.run(startup)
         for data in train_reader():
             exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
-            # the main program is runable, the datatype is fully supported
+            # the main program is runnable, the datatype is fully supported
             break
 
     def init_dtype(self):
@@ -66,7 +66,7 @@ def test_gpu(self):
         self.run_net_on_place(place)
 
 
-# TODO(dzhwinter): make sure the fp16 is runable
+# TODO(dzhwinter): make sure the fp16 is runnable
 # class TestFloat16(TestNetWithDtype):
 #     def init_dtype(self):
 #         self.dtype = "float16"

test/prim/pir_prim/test_pir_prim_flags.py

@@ -118,7 +118,7 @@ def train(self):
     def check_prim(self, net):
         program = net.forward.program_cache.last()[-1][-1].train_program
         if isinstance(
-            program, paddle.jit.dy2static.pir_partial_program.RunableProgram
+            program, paddle.jit.dy2static.pir_partial_program.RunnableProgram
         ):
             program = program.program
         block = program.global_block()