diff --git a/benchmark/fluid/fluid_benchmark.py b/benchmark/fluid/fluid_benchmark.py index 94ea7bd6aca7c9595037a2dacc5e36d4c77827e7..f8aed5a5e06c5e29dbdfb5db9f2ea0344c7eed6d 100644 --- a/benchmark/fluid/fluid_benchmark.py +++ b/benchmark/fluid/fluid_benchmark.py @@ -210,7 +210,7 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader, # generate fake: if args.use_fake_data: for var in feed_var_list: - v = startup_prog.global_block().clone_variable(var) + v = startup_prog.global_block()._clone_variable(var) var.persistable = True v.persistable = True diff --git a/doc/fluid/design/modules/python_api.md b/doc/fluid/design/modules/python_api.md index 265732a348ea77d21005e335390d99abcdfbd045..83af4e55485c079265d3f2b1e15070825b532c02 100644 --- a/doc/fluid/design/modules/python_api.md +++ b/doc/fluid/design/modules/python_api.md @@ -98,13 +98,13 @@ class Block(objects): def append_operator(self, ...): self.ops.append(Operator(self, ...)) - def prepend_operator(self, ...): # Parameter's ctor prepands initialize operators. + def _prepend_operator(self, ...): # Parameter's ctor prepands initialize operators. self.ops.prepend(Operator(self, ...)) ``` `create_parameter` is necessary because parameters are global variables, defined in the global block, but can be created in some sub-blocks. For example, an FC layer in the step block of an RNN operator. -`prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block. +`_prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block. ### Operator diff --git a/doc/fluid/howto/performance/error_clip.md b/doc/fluid/howto/performance/error_clip.md index 58aa73b8cd38d01e2426278a3479714e4fb6a3b0..749cf7693c75696feb17f8556224ed03649baa80 100644 --- a/doc/fluid/howto/performance/error_clip.md +++ b/doc/fluid/howto/performance/error_clip.md @@ -78,7 +78,7 @@ def error_clip_callback(block, context): op_desc = block.desc.op(block.desc.op_size() - 1) for grad_n in filter(lambda n: grad_to_var.has_key(n), op_desc.output_arg_names()): - fwd_var = block.var_recursive(grad_to_var[grad_n]) + fwd_var = block.__var_recursive(grad_to_var[grad_n]) error_clip = getattr(fwd_var, "error_clip", None) if not (error_clip is None or isinstance(error_clip, BaseErrorClipAttr)): diff --git a/paddle/contrib/float16/float16_transpiler.py b/paddle/contrib/float16/float16_transpiler.py index 91ba101edb65cd45bd5e37a0c6ad25e515593a81..66e0345c299730c113ffbdc8dd3c1fa32f872f3d 100644 --- a/paddle/contrib/float16/float16_transpiler.py +++ b/paddle/contrib/float16/float16_transpiler.py @@ -118,7 +118,7 @@ class Float16Transpiler: for var in self.block.vars.keys(): if var not in args: - self.block.remove_var(var) + self.block._remove_var(var) def _modify_feed_fetch(self): ''' @@ -165,7 +165,7 @@ class Float16Transpiler: dtype=core.VarDesc.VarType.FP16, shape=var.shape, persistable=var.persistable) - self.block.insert_op( + self.block._insert_op( i + 1, type="cast", inputs={"X": var}, @@ -188,7 +188,7 @@ class Float16Transpiler: persistable=var.persistable) find_op(var) var.op.rename_output(var_name, tmp_var_name) - self.block.insert_op( + self.block._insert_op( i, type="cast", inputs={"X": tmp_var}, @@ -253,4 +253,4 @@ class Float16Transpiler: # old var will be replaced by the fp16 var in program desc self.input_map[var.name] = fp16_var_name - self.block.remove_var(var.name) + self.block._remove_var(var.name) diff --git a/paddle/fluid/pybind/protobuf.cc b/paddle/fluid/pybind/protobuf.cc index fcd3356d44ee592233c3883d439d0677714900b8..2199f5311fd3728e624fc222a1b876eb947cc0aa 100644 --- a/paddle/fluid/pybind/protobuf.cc +++ b/paddle/fluid/pybind/protobuf.cc @@ -145,14 +145,14 @@ void BindBlockDesc(pybind11::module *m) { .def_property_readonly("id", &pd::BlockDesc::ID) .def_property_readonly("parent", &pd::BlockDesc::Parent) .def("get_forward_block_idx", &pd::BlockDesc::ForwardBlockID) - .def("set_forward_block_idx", &pd::BlockDesc::SetForwardBlockID) + .def("_set_forward_block_idx", &pd::BlockDesc::SetForwardBlockID) .def("append_op", &pd::BlockDesc::AppendOp, pybind11::return_value_policy::reference) - .def("prepend_op", &pd::BlockDesc::PrependOp, + .def("_prepend_op", &pd::BlockDesc::PrependOp, pybind11::return_value_policy::reference) - .def("insert_op", &pd::BlockDesc::InsertOp, + .def("_insert_op", &pd::BlockDesc::InsertOp, pybind11::return_value_policy::reference) - .def("remove_op", &pd::BlockDesc::RemoveOp) + .def("_remove_op", &pd::BlockDesc::RemoveOp) .def("var", [](pd::BlockDesc &self, pybind11::bytes byte_name) { std::string name = byte_name; @@ -165,7 +165,7 @@ void BindBlockDesc(pybind11::module *m) { return self.HasVar(name); }, pybind11::return_value_policy::reference) - .def("rename_var", + .def("_rename_var", [](pd::BlockDesc &self, const pybind11::bytes &byte_name, const pybind11::bytes &byte_name_new) { std::string name = byte_name; @@ -189,7 +189,7 @@ void BindBlockDesc(pybind11::module *m) { return self.FindVarRecursive(name); }, pybind11::return_value_policy::reference) - .def("remove_var", + .def("_remove_var", [](pd::BlockDesc &self, pybind11::bytes byte_name) { std::string name = byte_name; return self.RemoveVar(name); diff --git a/python/paddle/fluid/backward.py b/python/paddle/fluid/backward.py index ddcde04716d21df1f18e7202936f470d3d58a661..812f68bdd849544456b2e0ebf0b739f4f92b09ea 100644 --- a/python/paddle/fluid/backward.py +++ b/python/paddle/fluid/backward.py @@ -328,7 +328,7 @@ def _append_backward_ops_(block, if op.has_attr("sub_block"): sub_block = program.block(op.block_attr("sub_block")) grad_sub_block = program.create_block() - grad_sub_block.set_forward_block_idx(sub_block.idx) + grad_sub_block._set_forward_block_idx(sub_block.idx) cb = _callback_lookup_(op) if cb is not None: if callbacks is None: @@ -571,7 +571,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None, _append_backward_vars_(root_block, fwd_op_num, grad_to_var, grad_info_map) program.current_block_idx = current_block_idx - program.sync_with_cpp() + program._sync_with_cpp() # FIXME(zcd): prevent loss.grad optimized by mem_opt. loss.block.var(_append_grad_suffix_(loss.name)).persistable = True @@ -744,7 +744,7 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): _rename_grad_(block, fwd_op_num, grad_to_var, target_grad_map) _append_backward_vars_(block, fwd_op_num, grad_to_var, grad_info_map) - prog.sync_with_cpp() + prog._sync_with_cpp() grad_vars = [] for input_var in inputs: diff --git a/python/paddle/fluid/clip.py b/python/paddle/fluid/clip.py index d9acfef58c3ba92c763d195c88f1323b3c6512b9..c029662ebc1b7e7f7d1ea44b4ebd4b08b812a579 100644 --- a/python/paddle/fluid/clip.py +++ b/python/paddle/fluid/clip.py @@ -82,7 +82,7 @@ def error_clip_callback(block, context): op_desc = block.desc.op(block.desc.op_size() - 1) for grad_n in filter(lambda n: grad_to_var.has_key(n), op_desc.output_arg_names()): - fwd_var = block.var_recursive(grad_to_var[grad_n]) + fwd_var = block._var_recursive(grad_to_var[grad_n]) error_clip = getattr(fwd_var, "error_clip", None) if not (error_clip is None or isinstance(error_clip, BaseErrorClipAttr)): diff --git a/python/paddle/fluid/concurrency.py b/python/paddle/fluid/concurrency.py index 470dd0df524936a773f6e740c8079f0efa8ef7b4..b8fe9bd4c1988dd3f6fa82df391c3059dfbfcf93 100644 --- a/python/paddle/fluid/concurrency.py +++ b/python/paddle/fluid/concurrency.py @@ -69,8 +69,10 @@ class Go(BlockGuard): parent_block.append_op( type='go', inputs={ - 'X': - [parent_block.var_recursive(x_name) for x_name in x_name_list] + 'X': [ + parent_block._var_recursive(x_name) + for x_name in x_name_list + ] }, outputs={}, attrs={'sub_block': go_block}) @@ -259,7 +261,7 @@ class Select(BlockGuard): if var_name in intermediate ] - X = [select_block.var_recursive(x_name) for x_name in params] + X = [select_block._var_recursive(x_name) for x_name in params] # Needs to be used by `equal` inside the cases block. X.append(self.case_to_execute) diff --git a/python/paddle/fluid/executor.py b/python/paddle/fluid/executor.py index b436dfe70afdb52299222f8ba3f5bdff2842d103..f9e600cb4cb252baead87025db0e0db71e8169d2 100644 --- a/python/paddle/fluid/executor.py +++ b/python/paddle/fluid/executor.py @@ -309,7 +309,7 @@ class Executor(object): if not has_feed_operators(global_block, feed, feed_var_name): for i, name in enumerate(feed): out = global_block.var(name) - global_block.prepend_op( + global_block._prepend_op( type='feed', inputs={'X': [feed_var]}, outputs={'Out': [out]}, diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index d89cb246a939f247b94bc49f39198a909b1c30ea..03e0ac757586150610aee275620d9eee77323c99 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -32,7 +32,6 @@ except Exception, e: import unique_name __all__ = [ - 'Block', 'Variable', 'Program', 'Operator', @@ -447,7 +446,7 @@ class Operator(object): Notes: The constructor of operator should not be invoked directly. Use - Block.append_op or Block.prepend_op instead. + Block.append_op or Block._prepend_op instead. Examples: .. code-block:: python @@ -870,7 +869,7 @@ class Block(object): def forward_block_idx(self): return self.desc.get_forward_block_idx() - def set_forward_block_idx(self, idx): + def _set_forward_block_idx(self, idx): """ Set the forward block Idx. @@ -880,7 +879,7 @@ class Block(object): Returns: None """ - self.desc.set_forward_block_idx(idx) + self.desc._set_forward_block_idx(idx) @property def idx(self): @@ -909,7 +908,7 @@ class Block(object): raise ValueError("var %s not in this block" % name) return v - def var_recursive(self, name): + def _var_recursive(self, name): """ Get a Variable by name from this block recursively. @@ -951,9 +950,9 @@ class Block(object): raise ValueError("Var {0} is not found recursively".format(name)) def all_parameters(self): - return list(self.iter_parameters()) + return list(self._iter_parameters()) - def iter_parameters(self): + def _iter_parameters(self): return (item[1] for item in self.vars.iteritems() if isinstance(item[1], Parameter)) @@ -966,7 +965,7 @@ class Block(object): def has_var(self, name): return name in self.vars - def rename_var(self, name, new_name): + def _rename_var(self, name, new_name): """ Rename variable in vars and ops' inputs and outputs @@ -1000,8 +999,8 @@ class Block(object): else: raise ValueError("unsupported var type: %s", type(v)) orig_var_type = v.type - self.desc.rename_var(name, new_name) - # NOTE: v is destroyed by C++ after calling rename_var. + self.desc._rename_var(name, new_name) + # NOTE: v is destroyed by C++ after calling _rename_var. d = self.desc.find_var(new_name) if var_type == "Parameter": var = Parameter( @@ -1024,16 +1023,16 @@ class Block(object): error_clip=error_clip, stop_gradient=stop_gradient) - # rename the python side, sync_with_cpp will only add + # rename the python side, _sync_with_cpp will only add # new vars/ops to python side. self.vars[new_name] = var del self.vars[name] - self.sync_with_cpp() + self._sync_with_cpp() return var - def remove_var(self, name): - self.sync_with_cpp() - self.desc.remove_var(name) + def _remove_var(self, name): + self._sync_with_cpp() + self.desc._remove_var(name) del self.vars[name] def create_parameter(self, *args, **kwargs): @@ -1055,7 +1054,7 @@ class Block(object): self.ops.append(op) return op - def insert_op(self, index, *args, **kwargs): + def _insert_op(self, index, *args, **kwargs): """ Insert a Operator according to the giving arguments. @@ -1065,13 +1064,13 @@ class Block(object): Returns: Operator: the insert Operator. """ - self.sync_with_cpp() - op_desc = self.desc.insert_op(index) + self._sync_with_cpp() + op_desc = self.desc._insert_op(index) op = Operator(block=self, desc=op_desc, *args, **kwargs) self.ops.insert(index, op) return op - def remove_op(self, index): + def _remove_op(self, index): """ Remove the specific position operator. @@ -1081,11 +1080,11 @@ class Block(object): Returns: None """ - self.sync_with_cpp() - self.desc.remove_op(index, index + 1) + self._sync_with_cpp() + self.desc._remove_op(index, index + 1) del self.ops[index] - def slice_ops(self, start, end): + def _slice_ops(self, start, end): """ Return the Operator between start and end. @@ -1098,13 +1097,13 @@ class Block(object): """ return self.ops[start:end] - def prepend_op(self, *args, **kwargs): - op_desc = self.desc.prepend_op() + def _prepend_op(self, *args, **kwargs): + op_desc = self.desc._prepend_op() op = Operator(self, op_desc, *args, **kwargs) self.ops.insert(0, op) return op - def sync_with_cpp(self): + def _sync_with_cpp(self): """ Sync from the desc on the c++ end. This method is used to synchronize the c++ desc instance generated by backward. @@ -1170,7 +1169,7 @@ class Block(object): for index in range(len(self.ops)): assert self.ops[index].desc == ops_in_cpp[index] - def copy_param_info_from(self, other): + def _copy_param_info_from(self, other): """ Copy the information of parameters from the other block. @@ -1185,12 +1184,13 @@ class Block(object): None """ if not isinstance(other, Block): - raise TypeError("copy_param_info_from should be invoked with Block") - for p in other.iter_parameters(): + raise TypeError( + "_copy_param_info_from should be invoked with Block") + for p in other._iter_parameters(): assert isinstance(p, Parameter) v = self.vars.get(p.name, None) if v is None: - raise ValueError("copy_param_info_from should be invoked with " + raise ValueError("_copy_param_info_from should be invoked with " "same topology") assert isinstance(v, Variable) new_p = Parameter( @@ -1208,7 +1208,7 @@ class Block(object): name=v.name) self.vars[new_p.name] = new_p - def clone_variable(self, var): + def _clone_variable(self, var): """ Clone a variable into current block. @@ -1484,9 +1484,9 @@ class Program(object): p = Program() p.desc = core.ProgramDesc(self.desc) p.blocks = [Block(p, i) for i in xrange(self.desc.num_blocks())] - p.sync_with_cpp() + p._sync_with_cpp() - p.copy_param_info_from(self) + p._copy_param_info_from(self) p.copy_data_info_from(self) return p @@ -1536,7 +1536,7 @@ class Program(object): res = Program() res.desc = core.prune(self.desc, targets_idx) res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())] - res.sync_with_cpp() + res._sync_with_cpp() return res def inference_optimize(self): @@ -1562,7 +1562,7 @@ class Program(object): if op.has_attr('is_test'): op.set_attr('is_test', True) res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())] - res.sync_with_cpp() + res._sync_with_cpp() return res @staticmethod @@ -1582,7 +1582,7 @@ class Program(object): p = Program() p.desc = core.ProgramDesc(binary_str) p.blocks = [Block(p, i) for i in xrange(p.desc.num_blocks())] - p.sync_with_cpp() + p._sync_with_cpp() return p @property @@ -1662,7 +1662,7 @@ class Program(object): """ self.current_block_idx = self.current_block().parent_idx - def sync_with_cpp(self): + def _sync_with_cpp(self): """ Synchronize Python instance to its binding C++ object instance. If the program is modified in C++ space, this method should be invoked. @@ -1676,9 +1676,9 @@ class Program(object): for block_idx in range(len(self.blocks), self.desc.num_blocks()): self.blocks.append(Block(self, block_idx)) for block in self.blocks: - block.sync_with_cpp() + block._sync_with_cpp() - def copy_param_info_from(self, other): + def _copy_param_info_from(self, other): """ Copy the information of parameters from other program. @@ -1692,13 +1692,13 @@ class Program(object): None """ if not isinstance(other, Program): - raise TypeError("copy_param_info_from should be invoked with " + raise TypeError("_copy_param_info_from should be invoked with " "Program") if len(self.blocks) != len(other.blocks): - raise ValueError("copy_param_info_from should be invoked with two " + raise ValueError("_copy_param_info_from should be invoked with two " "program, with represent the same topology") - self.global_block().copy_param_info_from(other.global_block()) + self.global_block()._copy_param_info_from(other.global_block()) def copy_data_info_from(self, other): """ @@ -1714,11 +1714,11 @@ class Program(object): None """ if not isinstance(other, Program): - raise TypeError("copy_param_info_from should be invoked with " + raise TypeError("_copy_param_info_from should be invoked with " "Program") if len(self.blocks) != len(other.blocks): - raise ValueError("copy_param_info_from should be invoked with two " + raise ValueError("_copy_param_info_from should be invoked with two " "program, with represent the same topology") for var in other.global_block().vars.itervalues(): if var.is_data: diff --git a/python/paddle/fluid/initializer.py b/python/paddle/fluid/initializer.py index 373e9c060de1ee27c165ccd2380cd8c38612c4d9..0e640bf280d396504deec1183821da3e8a156530 100644 --- a/python/paddle/fluid/initializer.py +++ b/python/paddle/fluid/initializer.py @@ -148,7 +148,7 @@ class ConstantInitializer(Initializer): assert isinstance(var, framework.Variable) assert isinstance(block, framework.Block) # Initialization Ops should be prepended and not appended - op = block.prepend_op( + op = block._prepend_op( type="fill_constant", outputs={"Out": var}, attrs={ @@ -202,7 +202,7 @@ class UniformInitializer(Initializer): # Initialization Ops should be prepended and not appended if self._seed == 0: self._seed = block.program.random_seed - op = block.prepend_op( + op = block._prepend_op( type="uniform_random", outputs={"Out": var}, attrs={ @@ -256,7 +256,7 @@ class NormalInitializer(Initializer): # Initialization Ops should be prepended and not appended if self._seed == 0: self._seed = block.program.random_seed - op = block.prepend_op( + op = block._prepend_op( type="gaussian_random", outputs={"Out": var}, attrs={ @@ -346,7 +346,7 @@ class XavierInitializer(Initializer): if self._uniform: limit = np.sqrt(6.0 / float(fan_in + fan_out)) - op = block.prepend_op( + op = block._prepend_op( type="uniform_random", outputs={"Out": var}, attrs={ @@ -359,7 +359,7 @@ class XavierInitializer(Initializer): else: std = np.sqrt(2.0 / float(fan_in + fan_out)) - op = block.prepend_op( + op = block._prepend_op( type="gaussian_random", outputs={"Out": var}, attrs={ @@ -444,7 +444,7 @@ class MSRAInitializer(Initializer): if self._uniform: limit = np.sqrt(6.0 / float(fan_in)) - op = block.prepend_op( + op = block._prepend_op( type="uniform_random", outputs={"Out": var}, attrs={ @@ -457,7 +457,7 @@ class MSRAInitializer(Initializer): else: std = np.sqrt(2.0 / float(fan_in)) - op = block.prepend_op( + op = block._prepend_op( type="gaussian_random", outputs={"Out": var}, attrs={ diff --git a/python/paddle/fluid/io.py b/python/paddle/fluid/io.py index 0eb1194e2754331dcbc8436f6680ab776a999c29..cf43998228a06d73f4d7d6dfc85dcd002078ba0f 100644 --- a/python/paddle/fluid/io.py +++ b/python/paddle/fluid/io.py @@ -523,7 +523,7 @@ def prepend_feed_ops(inference_program, for i, name in enumerate(feed_target_names): out = global_block.var(name) - global_block.prepend_op( + global_block._prepend_op( type='feed', inputs={'X': [feed_var]}, outputs={'Out': [out]}, @@ -625,7 +625,7 @@ def save_inference_model(dirname, for i, op in enumerate(global_block.ops): op.desc.set_is_target(False) if op.type == "feed" or op.type == "fetch": - global_block.remove_op(i) + global_block._remove_op(i) copy_program.desc.flush() pruned_program = copy_program.prune(targets=target_vars) @@ -874,7 +874,7 @@ def get_test_program(filelist, program=None, startup_program=None): main_block = program.global_block() for var in main_block.vars.values(): if var.type == core.VarDesc.VarType.READER: - main_block.rename_var( + main_block._rename_var( str(var.name), str(_get_test_reader_name(var.name))) for op in main_block.ops: @@ -883,7 +883,7 @@ def get_test_program(filelist, program=None, startup_program=None): if op.type == "create_multi_pass_reader": test_op.set_attr("pass_num", 1) - startup_program.sync_with_cpp() - program.sync_with_cpp() + startup_program._sync_with_cpp() + program._sync_with_cpp() return program diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py index 849474dc58461ac3772f439da7bf5d57592daa8c..782aa933f2ee86274e800045c9356d8072915fc1 100644 --- a/python/paddle/fluid/layers/control_flow.py +++ b/python/paddle/fluid/layers/control_flow.py @@ -730,8 +730,10 @@ class While(object): parent_block.append_op( type='while', inputs={ - 'X': - [parent_block.var_recursive(x_name) for x_name in x_name_list], + 'X': [ + parent_block._var_recursive(x_name) + for x_name in x_name_list + ], 'Condition': [self.cond_var] }, outputs={'Out': out_vars, @@ -1259,7 +1261,7 @@ class ConditionalBlock(object): input_set = set([ipt.name for ipt in self.inputs]) param_list = [ - parent_block.var_recursive(each_name) for each_name in params + parent_block._var_recursive(each_name) for each_name in params if each_name not in input_set ] diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index cc223899c73deb173701db0fba4123c8442bfd43..56124663929d1e33b7144ab57ae3b3c55e1652b3 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -4367,7 +4367,7 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1): helper.set_variable_initializer( counter, initializer=Constant( value=begin - 1, force_cpu=True)) - helper.main_program.global_block().prepend_op( + helper.main_program.global_block()._prepend_op( type='increment', inputs={'X': [counter]}, outputs={'Out': [counter]}, diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py index 94e78d155f1c9aa5b7abda0e83db528ad5e2aafb..b1762c2b46cef4f84216d88f66c68b6fbcb0b476 100644 --- a/python/paddle/fluid/optimizer.py +++ b/python/paddle/fluid/optimizer.py @@ -240,7 +240,7 @@ class Optimizer(object): self._finish_update(loss.block, parameters_and_grads) end = len(global_block.ops) - return global_block.slice_ops(start, end) + return global_block._slice_ops(start, end) def minimize(self, loss, diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index 6baf648198585022f992709c519038688af293e1..10028a8c6e33edcea27650d925ca7378b770f143 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -152,7 +152,7 @@ class ParallelExecutor(object): self.executor = core.ParallelExecutor( self._places, set([ - p.name for p in main.global_block().iter_parameters() + p.name for p in main.global_block()._iter_parameters() if not p.stop_gradient ]), set(self.persistable_vars), main.desc, loss_name diff --git a/python/paddle/fluid/tests/unittests/test_protobuf_descs.py b/python/paddle/fluid/tests/unittests/test_protobuf_descs.py index 3f9059fb5b31cd009c068ccddc9a8938adae5772..f75a79bfa42405747df9e6f4f4ab743014e303b9 100644 --- a/python/paddle/fluid/tests/unittests/test_protobuf_descs.py +++ b/python/paddle/fluid/tests/unittests/test_protobuf_descs.py @@ -181,13 +181,13 @@ class TestBlockDesc(unittest.TestCase): self.assertIsNotNone(block) op1 = block.append_op() op2 = block.append_op() - op0 = block.prepend_op() + op0 = block._prepend_op() all_ops = [] for idx in xrange(0, block.op_size()): all_ops.append(block.op(idx)) self.assertEqual(all_ops, [op0, op1, op2]) - def test_remove_op(self): + def test__remove_op(self): program = Program() program_desc = program.desc self.assertIsNotNone(program_desc) @@ -201,8 +201,8 @@ class TestBlockDesc(unittest.TestCase): op1.set_type("test") op2.set_type("test") - block.remove_op(1, 2) - program.sync_with_cpp() + block._remove_op(1, 2) + program._sync_with_cpp() all_ops = [] for idx in xrange(0, block.op_size()): diff --git a/python/paddle/fluid/transpiler/details/program_utils.py b/python/paddle/fluid/transpiler/details/program_utils.py index f10b496306a002ee131d01798a0698b807d379ca..2ca1d4716b103d17117ae3ee958667c3a9747cdf 100644 --- a/python/paddle/fluid/transpiler/details/program_utils.py +++ b/python/paddle/fluid/transpiler/details/program_utils.py @@ -17,10 +17,10 @@ def delete_ops(block, ops): try: start = list(block.ops).index(ops[0]) end = list(block.ops).index(ops[-1]) - [block.remove_op(start) for _ in xrange(end - start + 1)] + [block._remove_op(start) for _ in xrange(end - start + 1)] except Exception, e: raise e - block.program.sync_with_cpp() + block.program._sync_with_cpp() def find_op_by_input_arg(block, arg_name): diff --git a/python/paddle/fluid/transpiler/distribute_transpiler.py b/python/paddle/fluid/transpiler/distribute_transpiler.py index 006510a7d3f4e67f4f43ec794a5d2aa10f80fc37..c2044bf03135dd9c5256021d87866cfbbc598dad 100644 --- a/python/paddle/fluid/transpiler/distribute_transpiler.py +++ b/python/paddle/fluid/transpiler/distribute_transpiler.py @@ -243,7 +243,7 @@ class DistributeTranspiler(object): AssertionError("Can not insert the send op by original " "variable name :", orig_varname) - program.global_block().insert_op( + program.global_block()._insert_op( index=index + 1, type="send", inputs={"X": splited_vars}, @@ -429,7 +429,7 @@ class DistributeTranspiler(object): # clone vars for var in origin_block.vars: - new_sub_block.clone_variable(var) + new_sub_block._clone_variable(var) # clone ops for origin_op in origin_block.ops: @@ -525,7 +525,7 @@ class DistributeTranspiler(object): outputs={}, attrs=attrs) - pserver_program.sync_with_cpp() + pserver_program._sync_with_cpp() return pserver_program def get_startup_program(self, endpoint, pserver_program): @@ -557,7 +557,7 @@ class DistributeTranspiler(object): pserver_vars = pserver_program.global_block().vars created_var_map = dict() for _, var in pserver_vars.iteritems(): - tmpvar = s_prog.global_block().clone_variable(var) + tmpvar = s_prog.global_block()._clone_variable(var) created_var_map[var.name] = tmpvar # 2. rename op outputs @@ -760,7 +760,7 @@ class DistributeTranspiler(object): self.all_prefetch_output_vars.append(prefetch_output_vars) # insert split_ids_op - program.global_block().insert_op( + program.global_block()._insert_op( index=lookup_table_op_index, type="split_ids", inputs={ @@ -772,7 +772,7 @@ class DistributeTranspiler(object): outputs={"Out": prefetch_input_vars}) # insert prefetch_op - program.global_block().insert_op( + program.global_block()._insert_op( index=lookup_table_op_index + 1, type="prefetch", inputs={'X': prefetch_input_vars}, @@ -783,7 +783,7 @@ class DistributeTranspiler(object): }) # insert concat_op - program.global_block().insert_op( + program.global_block()._insert_op( index=lookup_table_op_index + 2, type="merge_ids", inputs={ @@ -814,14 +814,14 @@ class DistributeTranspiler(object): if table_grad_name in op.output_arg_names: op_index = list(all_ops).index(op) # insert split_ids_op - program.global_block().insert_op( + program.global_block()._insert_op( index=op_index + 1, type="split_ids", inputs={ 'Ids': [program.global_block().vars[table_grad_name]] }, outputs={"Out": self.trainer_side_table_grad_list}) - program.global_block().insert_op( + program.global_block()._insert_op( index=op_index + 2, type="send", inputs={'X': self.trainer_side_table_grad_list}, @@ -880,7 +880,7 @@ class DistributeTranspiler(object): persistable=True) # parameter must be selected rows param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS) - grad_var = pserver_program.global_block().clone_variable( + grad_var = pserver_program.global_block()._clone_variable( self.origin_program.global_block().vars[grad_var_name( self.table_name)]) @@ -920,7 +920,7 @@ class DistributeTranspiler(object): if not splited_grad_name.startswith(origin_grad_name): raise ValueError("origin_grad_var: " + splited_grad_name + " grad_var:" + grad_var.name) - grad_var = pserver_program.global_block().rename_var( + grad_var = pserver_program.global_block()._rename_var( origin_grad_name, splited_grad_name) lr_var = pserver_program.global_block().vars[table_opt_op.input( @@ -996,7 +996,7 @@ class DistributeTranspiler(object): if self.sync_mode and add_trainer_suffix: new_var_name = "%s.trainer_%d" % \ (orig_var.name, self.trainer_id) - program.global_block().rename_var(varname, new_var_name) + program.global_block()._rename_var(varname, new_var_name) var_mapping[varname] = \ [program.global_block().var(new_var_name)] else: @@ -1030,8 +1030,7 @@ class DistributeTranspiler(object): type=orig_var.type, shape=splited_shape) # flattend splited var var_mapping[varname].append(var) - program.global_block().sync_with_cpp() - + program.global_block()._sync_with_cpp() return var_mapping def create_splited_vars(self, source_var, block, tag): @@ -1059,7 +1058,7 @@ class DistributeTranspiler(object): height_sections = [] for v in splited_vars: height_sections.append(v.shape[0]) - program.global_block().insert_op( + program.global_block()._insert_op( index=index + 1, type="split_selected_rows", inputs={"X": orig_var}, @@ -1069,7 +1068,7 @@ class DistributeTranspiler(object): sections = [] for v in splited_vars: sections.append(v.shape[0]) - program.global_block().insert_op( + program.global_block()._insert_op( index=index + 1, type="split_byref", inputs={"X": orig_var}, @@ -1258,7 +1257,7 @@ class DistributeTranspiler(object): varlist = [varlist] for var in varlist: if var not in program.global_block().vars: - block.clone_variable(var) + block._clone_variable(var) outputs = self._get_output_map_from_op( self.origin_program.global_block().vars, op) @@ -1267,7 +1266,7 @@ class DistributeTranspiler(object): varlist = [varlist] for var in varlist: if var not in program.global_block().vars: - block.clone_variable(var) + block._clone_variable(var) return block.append_op( type=op.type, inputs=inputs, outputs=outputs, attrs=op.attrs) @@ -1305,7 +1304,7 @@ class DistributeTranspiler(object): if grad_block: outputs[key] = grad_block elif not program.global_block().vars.has_key(var.name): - program.global_block().clone_variable(var) + program.global_block()._clone_variable(var) return optimize_block.append_op( type=opt_op.type, diff --git a/python/paddle/fluid/transpiler/inference_transpiler.py b/python/paddle/fluid/transpiler/inference_transpiler.py index b8afeae5ebd6ef7948a7c0c2775f419af461da04..f1905f08787da7a58a41d840ea68fb6c07f4028f 100644 --- a/python/paddle/fluid/transpiler/inference_transpiler.py +++ b/python/paddle/fluid/transpiler/inference_transpiler.py @@ -95,7 +95,7 @@ class InferenceTranspiler(object): # modify bnorm OP to include relu current_op.set_attr("fuse_with_relu", True) # remove relu OP - self.block.remove_op(i + 1) + self.block._remove_op(i + 1) i = i + 1 self._remove_unused_var() @@ -171,7 +171,7 @@ class InferenceTranspiler(object): # fuse batch_norm self._fuse_param(current_op, next_op, bias_op, 0) # remove batch_norm_op - self.block.remove_op(i + 2) + self.block._remove_op(i + 2) i = i + 1 # conv2d with bias, the next_op.type is elementwise_add elif (next_op.type == 'elementwise_add'): @@ -180,7 +180,7 @@ class InferenceTranspiler(object): # fuse batch_norm self._fuse_param(current_op, next_next_op, next_op, 1) # remove batch_norm_op - self.block.remove_op(i + 2) + self.block._remove_op(i + 2) i = i + 1 i = i + 1 @@ -212,7 +212,7 @@ class InferenceTranspiler(object): y_var = self.block.var(bn_op.input("Bias")[0]) out_var = self.block.var(bn_op.output("Y")[0]) - bias_op = self.block.insert_op( + bias_op = self.block._insert_op( index, type="elementwise_add", inputs={"X": x_var, @@ -307,4 +307,4 @@ class InferenceTranspiler(object): for var in self.block.vars.keys(): if var not in args: - self.block.remove_var(var) + self.block._remove_var(var) diff --git a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py index 999ef43ca0feacbddff5f9db59589ce7097fe77e..dd90d66110e6233806b04bb726636a915f2ad84a 100644 --- a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py +++ b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py @@ -177,7 +177,7 @@ class ControlFlowGraph(object): in_diff) if can_optimize: index = i + fwd_id + 1 if is_forward else i - self._forward_num + bwd_id + 1 - delete_op = block_desc.insert_op(index) + delete_op = block_desc._insert_op(index) delete_op.set_type("delete_var") delete_op.set_input("X", can_optimize) if is_forward: