# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ..wrapped_decorator import signature_safe_contextmanager, wrap_decorator import contextlib import numpy as np from paddle.fluid import core from paddle.fluid import framework from .tracer import Tracer import logging import objgraph __all__ = [ 'no_grad', 'guard', 'to_variable', ] def _switch_to_static_graph_(func): def __impl__(*args, **kwargs): with framework._dygraph_guard(None): return func(*args, **kwargs) return __impl__ switch_to_static_graph = wrap_decorator(_switch_to_static_graph_) @signature_safe_contextmanager def program_desc_tracing_guard(enable): tracer = framework._dygraph_tracer() if tracer: original_val = tracer._enable_program_desc_tracing tracer._enable_program_desc_tracing = enable yield if tracer: tracer._enable_program_desc_tracing = original_val # This function should be removed in V1.6, because it can easily lead to cyclic dependencies. def enabled(): # Internal use only return framework.in_dygraph_mode() @contextlib.contextmanager def _switch_tracer_mode_guard_(is_train=True): tracer = framework._dygraph_tracer() if tracer: mode = tracer._train_mode tracer._train_mode = is_train yield tracer._train_mode = mode else: yield def _no_grad_(func): """ This Decorator will avoid the func being decorated creating backward network in dygraph mode Parameter: - **func** (python func): the func don't need grad Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid @fluid.dygraph.no_grad def test_layer(): with fluid.dygraph.guard(): inp = np.ones([3, 32, 32], dtype='float32') t = fluid.dygraph.base.to_variable(inp) fc1 = fluid.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1) fc2 = fluid.FC('fc2', size=4) ret = fc1(t) dy_ret = fc2(ret) test_layer() """ def __impl__(*args, **kwargs): with _switch_tracer_mode_guard_(is_train=False): return func(*args, **kwargs) return __impl__ no_grad = wrap_decorator(_no_grad_) # for fluidDoc no_grad.__doc__ = _no_grad_.__doc__ @signature_safe_contextmanager def guard(place=None): """ This context will create a dygraph context for dygraph to run, using python ``with`` statement. Parameters: place(fluid.CPUPlace or fluid.CUDAPlace, optional): Place to execute dygraph. If None, the running place will be determined according to the way of paddle compilation. Default: None return: None Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid with fluid.dygraph.guard(): inp = np.ones([3, 32, 32], dtype='float32') t = fluid.dygraph.base.to_variable(inp) fc1 = fluid.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1) fc2 = fluid.FC('fc2', size=4) ret = fc1(t) dy_ret = fc2(ret) """ train = framework.Program() startup = framework.Program() tracer = Tracer() VarBase = core.VarBase if place is None: if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() tracer._expected_place = place with framework.program_guard(train, startup): with framework.unique_name.guard(): with framework._dygraph_guard(tracer): with framework._dygraph_place_guard(place): yield def _print_debug_msg(parameter_list, limit=5, is_test=False): if not core._is_dygraph_debug_enabled(): logging.warn( 'Debug mode is not enabled. Please set FLAGS_dygraph_debug=1 to enable debug' ) return unique_name_size = len(framework.unique_name.generator.ids) tracer_var_size = len(parameter_list) alive_cpp_var_size = len(core.VarBase._alive_vars()) if not is_test: logging.warn( 'unique_name num: {}, tracer vars num: {}, alive cpp vars num: {}' .format(unique_name_size, tracer_var_size, alive_cpp_var_size)) objgraph.show_growth(limit=limit) else: return unique_name_size, tracer_var_size, alive_cpp_var_size @framework.dygraph_only def to_variable(value, name=None, zero_copy=None): """ The API will create a ``Variable`` object from numpy\.ndarray or Variable object. Parameters: value(ndarray|Variable): The numpy\.ndarray or Variable object that needs to be converted, it can be multi-dimension, and the data type is one of numpy\.{float16, float32, float64, int16, int32, int64, uint8, uint16}. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` zero_copy(bool, optional): Whether to share memory with the input numpy array. This parameter only works with CPUPlace and will be set to True when it is None. Default: None. Returns: Variable: If ``value`` is a numpy\.ndarray object, return ``Tensor`` created from the specified numpy\.ndarray object, which has same data type and shape with ``value``. If ``value`` is a Variable object, just return ``value``. Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid with fluid.dygraph.guard(fluid.CPUPlace()): x = np.ones([2, 2], np.float32) y = fluid.dygraph.to_variable(x, zero_copy=False) x[0][0] = -1 y[0][0].numpy() # array([1.], dtype=float32) y = fluid.dygraph.to_variable(x) x[0][0] = 0 y[0][0].numpy() # array([0.], dtype=float32) """ if isinstance(value, np.ndarray): assert framework.in_dygraph_mode( ), "to_variable could only be called in dygraph mode" if isinstance(framework._current_expected_place(), framework.core.CPUPlace): if zero_copy is None: zero_copy = True else: assert not zero_copy, "zero_copy mode can only be used with CPUPlace" zero_copy = False py_var = core.VarBase( value=value, place=framework._current_expected_place(), persistable=False, zero_copy=zero_copy, name=name if name else '') return py_var elif isinstance(value, (core.VarBase, framework.Variable)): return value else: raise TypeError( "to_variable only accepts 'ndarray' and 'Variable' as value's input")