[Fluid clean]clean distributed fluid API (#49795)

python/paddle/distributed/fleet/metrics/metric.py

@@ -38,11 +38,11 @@ def sum(input, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          input = fluid.layers.cast(some_input, dtype='float32')
+          input = paddle.cast(some_input, dtype='float32')
           cnt = paddle.sum(input)
           global_cnt = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp = paddle.add(cnt, global_cnt)
-          fluid.layers.assign(tmp, global_cnt)
+          paddle.assign(tmp, global_cnt)
 
           # in train.py, after train or infer
           res = np.array(scope.find_var(global_cnt.name).get_tensor())
@@ -78,11 +78,11 @@ def max(input, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          input = fluid.layers.cast(some_input, dtype='float32')
+          input = paddle.cast(some_input, dtype='float32')
           cnt = paddle.sum(input)
           global_cnt = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp = paddle.maximum(cnt, global_cnt)
-          fluid.layers.assign(tmp, global_cnt)
+          paddle.assign(tmp, global_cnt)
 
           # in train.py, after train or infer
           res = np.array(scope.find_var(global_cnt.name).get_tensor())
@@ -118,11 +118,11 @@ def min(input, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          input = fluid.layers.cast(some_input, dtype='float32')
+          input = paddle.cast(some_input, dtype='float32')
           cnt = paddle.sum(input)
           global_cnt = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp = paddle.minimum(cnt, global_cnt)
-          fluid.layers.assign(tmp, global_cnt)
+          paddle.assign(tmp, global_cnt)
 
           # in train.py, after train or infer
           res = np.array(scope.find_var(global_cnt.name).get_tensor())
@@ -159,9 +159,9 @@ def auc(stat_pos, stat_neg, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          similarity_norm = fluid.layers.sigmoid(paddle.clip(output, min=-15.0, max=15.0))
-          binary_predict = fluid.layers.concat(
-              input=[paddle.subtract(fluid.layers.ceil(similarity_norm), similarity_norm), similarity_norm], axis=1)
+          similarity_norm = paddle.nn.functional.sigmoid(paddle.clip(output, min=-15.0, max=15.0))
+          binary_predict = paddle.concat(
+              input=[paddle.subtract(paddle.ceil(similarity_norm), similarity_norm), similarity_norm], axis=1)
           self.auc, batch_auc, [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg] =
               paddle.static.auc(input=binary_predict, label=label, curve='ROC', num_thresholds=4096)
 
@@ -231,7 +231,7 @@ def mae(abserr, total_ins_num, scope=None, util=None):
     distributed mae in fleet
 
     Args:
-        abserr(numpy.array|Variable|string): abserr in output of fluid.contrib.layers.ctr_metric_bundle
+        abserr(numpy.array|Variable|string): abserr in output of paddle.static.ctr_metric_bundle
         total_ins_num(numpy.array|Variable|string): total variable
         scope(Scope): specific scope
 
@@ -242,7 +242,7 @@ def mae(abserr, total_ins_num, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
+          sqrerr, abserr, prob, q, pos, total = paddle.static.ctr_metric_bundle(similarity_norm, paddle.cast(x=label, dtype='float32'))
 
           # in train.py, after train or infer
           res = np.array(scope.find_var(abserr.name).get_tensor())
@@ -281,7 +281,7 @@ def rmse(sqrerr, total_ins_num, scope=None, util=None):
     distributed rmse in fleet
 
     Args:
-        sqrerr(numpy.array|Variable|string): sqrerr in output of fluid.contrib.layers.ctr_metric_bundle
+        sqrerr(numpy.array|Variable|string): sqrerr in output of paddle.static.ctr_metric_bundle
         total_ins_num(numpy.array|Variable|string): total variable
         scope(Scope): specific scope
 
@@ -292,7 +292,7 @@ def rmse(sqrerr, total_ins_num, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
+          sqrerr, abserr, prob, q, pos, total = paddle.static.ctr_metric_bundle(similarity_norm, paddle.cast(x=label, dtype='float32'))
 
           # in train.py, after train or infer
           res = np.array(scope.find_var(sqrerr.name).get_tensor())
@@ -331,7 +331,7 @@ def mse(sqrerr, total_ins_num, scope=None, util=None):
     distributed mse in fleet
 
     Args:
-        sqrerr(numpy.array|Variable|string): sqrerr in output of fluid.contrib.layers.ctr_metric_bundle
+        sqrerr(numpy.array|Variable|string): sqrerr in output of paddle.static.ctr_metric_bundle
         total_ins_num(numpy.array|Variable|string): total variable
         scope(Scope): specific scope
 
@@ -342,7 +342,7 @@ def mse(sqrerr, total_ins_num, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          sqrerr, abserr, prob, q, pos, total = fluid.contrib.layers.ctr_metric_bundle(similarity_norm, fluid.layers.cast(x=label, dtype='float32'))
+          sqrerr, abserr, prob, q, pos, total = paddle.static.ctr_metric_bundle(similarity_norm, paddle.cast(x=label, dtype='float32'))
 
           # in train.py, after train or infer
           metric = np.array(scope.find_var(sqrerr.name).get_tensor())
@@ -393,15 +393,15 @@ def acc(correct, total, scope=None, util=None):
           # in model.py
           correct = paddle.static.create_global_var(dtype='float32', shape=[1], value=0)
           total = paddle.static.create_global_var(dtype='float32', shape=[1], value=0)
-          acc = fluid.layers.acc(predict, label, k=1, correct=correct, total=total)
+          acc = paddle.metric.accuracy(predict, label, k=1, correct=correct, total=total)
 
           global_correct = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp1 = paddle.minimum(correct, global_correct)
-          fluid.layers.assign(tmp1, global_correct)
+          paddle.assign(tmp1, global_correct)
 
           global_total = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp2 = paddle.minimum(total, global_total)
-          fluid.layers.assign(tmp2, global_total)
+          paddle.assign(tmp2, global_total)
 
           # in train.py, after train or infer
           correct_num = np.array(scope.find_var(correct.name).get_tensor())

python/paddle/distributed/fleet/runtime/parameter_server_runtime.py

@@ -15,12 +15,19 @@
 import os
 import warnings
 
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
-from paddle.fluid.compiler import CompiledProgram
-from paddle.fluid.executor import Executor
-from paddle.fluid.framework import Program, Variable
-from paddle.fluid.parallel_executor import ParallelExecutor
+from paddle.static import (
+    CompiledProgram,
+    Executor,
+    ParallelExecutor,
+    Program,
+    Variable,
+    default_main_program,
+    default_startup_program,
+    save_inference_model,
+)
 
 from ..base.private_helper_function import wait_server_ready
 from .runtime_base import RuntimeBase
@@ -90,7 +97,7 @@ class ParameterServerRuntime(RuntimeBase):
             return var.name in varnames
 
         load_vars = list(
-            filter(_in_varnames, fluid.default_main_program().list_vars())
+            filter(_in_varnames, default_main_program().list_vars())
         )
         if main_program is None:
             main_program = self.origin_main_program
@@ -130,7 +137,7 @@ class ParameterServerRuntime(RuntimeBase):
         executor.run(load_prog)
 
     def _load_distributed_params(self, dirname, varnames):
-        from paddle.fluid.communicator import LargeScaleKV
+        from paddle.distributed.communicator import LargeScaleKV
         from paddle.fluid.incubate.fleet.parameter_server.ir.public import (
             _get_varname_parts,
         )
@@ -202,7 +209,7 @@ class ParameterServerRuntime(RuntimeBase):
 
                 if len(dist_varnames) != 0:
                     raise ValueError(
-                        "GeoStrategy can not support large scale embeding now, please use fluid.layers.embedding"
+                        "GeoStrategy can not support large scale embeding now, please use paddle.static.nn.embedding"
                     )
 
                 init_attrs = []
@@ -284,7 +291,7 @@ class ParameterServerRuntime(RuntimeBase):
                 recv_type=1
             )
 
-        from paddle.fluid.communicator import Communicator
+        from paddle.distributed.communicator import Communicator
 
         self._communicator = Communicator(
             trainer_config.mode, kwargs, trainer_config.get_communicator_flags()
@@ -297,7 +304,7 @@ class ParameterServerRuntime(RuntimeBase):
             warnings.warn("communicator has been initialized, skip")
 
     def _get_executor(self):
-        executor = fluid.Executor(fluid.CPUPlace())
+        executor = Executor(paddle.CPUPlace())
         if self.role_maker._is_heter_parameter_server_mode:
             heter_worker_device_guard = (
                 self.context["valid_strategy"]
@@ -313,13 +320,13 @@ class ParameterServerRuntime(RuntimeBase):
             if self.role_maker._is_heter_worker():
                 if heter_worker_device_guard == "GPU":
                     executor = Executor(
-                        fluid.CUDAPlace(
+                        paddle.CUDAPlace(
                             int(os.getenv("FLAGS_selected_gpus", "0"))
                         )
                     )
                 elif heter_worker_device_guard == "XPU":
                     executor = Executor(
-                        fluid.XPUPlace(
+                        paddle.XPUPlace(
                             int(os.getenv("FLAGS_selected_xpus", "0"))
                         )
                     )
@@ -340,7 +347,7 @@ class ParameterServerRuntime(RuntimeBase):
         ):
             # for heter trainer wait server ready
             wait_server_ready(self.role_maker._get_pserver_endpoints())
-        executor.run(fluid.default_startup_program())
+        executor.run(default_startup_program())
 
         if self.role_maker._is_heter_worker():
             self._init_worker()
@@ -375,7 +382,7 @@ class ParameterServerRuntime(RuntimeBase):
                     + sparse_related_optimize_varnames
                     + distributed_related_optimize_varnames
                 ),
-                fluid.default_main_program().list_vars(),
+                default_main_program().list_vars(),
             )
         )
 
@@ -386,9 +393,9 @@ class ParameterServerRuntime(RuntimeBase):
             raise ValueError("There is no directory named '%s'", model_dirname)
 
         # load dense
-        fluid.io.load_vars(
+        paddle.static.load_vars(
             executor,
-            main_program=fluid.default_main_program(),
+            main_program=default_main_program(),
             dirname=model_dirname,
             vars=remaining_vars,
         )
@@ -409,7 +416,7 @@ class ParameterServerRuntime(RuntimeBase):
 
     def _run_server(self):
         executor = self._get_executor()
-        executor.run(fluid.default_main_program())
+        executor.run(default_main_program())
 
     def _stop_worker(self):
         self._communicator.stop()
@@ -671,7 +678,7 @@ class ParameterServerRuntime(RuntimeBase):
             )
         )
 
-        fluid.io.save_vars(
+        paddle.static.save_vars(
             executor,
             main_program=main_program,
             dirname=dirname,
@@ -743,7 +750,7 @@ class ParameterServerRuntime(RuntimeBase):
                 raise TypeError(
                     "in fleet.save_inference_model() function, main_program must be as Program type, CompiledProgram is not allowed"
                 )
-            fluid.io.save_inference_model(
+            save_inference_model(
                 dirname,
                 feeded_var_names,
                 target_vars,
@@ -754,7 +761,7 @@ class ParameterServerRuntime(RuntimeBase):
                 export_for_deployment,
             )
         else:
-            fluid.io.save_inference_model(
+            save_inference_model(
                 dirname,
                 feeded_var_names,
                 target_vars,
@@ -773,7 +780,7 @@ class ParameterServerRuntime(RuntimeBase):
                 program_desc_str = f.read()
 
             program = Program.parse_from_string(program_desc_str)
-            program._copy_dist_param_info_from(fluid.default_main_program())
+            program._copy_dist_param_info_from(default_main_program())
             self._ps_inference_save_persistables(
                 executor, dirname, program, mode=0
             )

python/paddle/distributed/passes/ps_trainer_pass.py

@@ -17,10 +17,10 @@ import os
 from _collections import defaultdict
 
 import paddle
+import paddle.fluid.framework as framework
 from paddle.distributed.passes.pass_base import PassBase, register_pass
-from paddle.fluid.framework import Parameter
 from paddle.framework import core
-from paddle.static import Program
+from paddle.static import Parameter, Program
 
 from ..ps.utils.collective_transpiler import SingleProcessMultiThread
 from ..ps.utils.public import *  # noqa: F403
@@ -757,7 +757,7 @@ class PsGpuPass(PassBase):
                 )
                 new_op_desc.copy_from(op_desc)
                 new_op_desc._set_attr(op_role_attr_name, backward)
-                new_op = paddle.fluid.framework.Operator(
+                new_op = paddle.static.Operator(
                     program.global_block(), new_op_desc
                 )
                 program.global_block().ops.insert(insert_index + 1, new_op)

python/paddle/distributed/ps/utils/ps_program_builder.py

@@ -472,7 +472,7 @@ class FlPsProgramBuilder(HeterAsyncPsProgramBuilder):
         if not self.is_server:
             self._build_trainer_programs()
             fluid.framework.switch_startup_program(self.cloned_startup)
-            fluid.framework.switch_main_program(self.cloned_main)
+            paddle.framework.switch_main_program(self.cloned_main)
             print(
                 "paddle.static.default_startup_program: {}".format(
                     paddle.static.default_startup_program()._heter_pipeline_opt
@@ -483,4 +483,4 @@ class FlPsProgramBuilder(HeterAsyncPsProgramBuilder):
             fluid.framework.switch_startup_program(
                 self.attrs['_startup_server']
             )
-            fluid.framework.switch_main_program(self.attrs['_main_server'])
+            paddle.framework.switch_main_program(self.attrs['_main_server'])

python/paddle/distributed/ps/utils/public.py

@@ -18,8 +18,8 @@ import os
 import warnings
 from functools import reduce
 
-import paddle.fluid as fluid
-import paddle.fluid.framework as framework
+from paddle.distributed.io import is_persistable
+from paddle.fluid.framework import generate_control_dev_var_name
 from paddle.framework import core
 
 # logging.basicConfig(
@@ -1253,7 +1253,7 @@ def screen_persistables(program, var_list):
         else:
             var = program.global_block().vars[var_name]
 
-        if fluid.io.is_persistable(var):
+        if is_persistable(var):
             need_remove.append(var_name)
 
     for var_name in need_remove:
@@ -1676,9 +1676,7 @@ def add_send_op(program, block, _vars):
         table_dict[table_id]['var_list'].append(persistable_var)
 
     for table_id in table_dict:
-        dummy_output = block.create_var(
-            name=framework.generate_control_dev_var_name()
-        )
+        dummy_output = block.create_var(name=generate_control_dev_var_name())
         send_input_vars = [
             block.vars[union_var]
             for union_var in table_dict[table_id]['var_list']