Source code for paddle.fluid.dygraph.base

# 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
import os

from paddle.fluid import core
from paddle.fluid import framework
from .tracer import Tracer
import logging

__all__ = [
    'enabled',
    'no_grad',
    'guard',
    'to_variable',
]


[docs]def enabled(): 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 _dygraph_not_support_(func): def __impl__(*args, **kwargs): assert not framework.in_dygraph_mode( ), "We don't support %s in Dygraph mode" % func.__name__ return func(*args, **kwargs) return __impl__ def _no_grad_(func): """ This Decorator will avoid the func being decorated creating backward network in dygraph mode Args: 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_) _not_support = wrap_decorator(_dygraph_not_support_) @signature_safe_contextmanager
[docs]def guard(place=None): """ This context will create a dygraph context for dygraph to run Args: place(fluid.CPUPlace|fluid.CUDAPlace|None): Place to run 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(train.current_block().desc) if place is None: if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() 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(): 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(framework._dygraph_tracer()._vars) alive_cpp_var_size = len(core.VarBase._alive_vars()) logging.warn( 'unique_name num: {}, tracer vars num: {}, alive cpp vars num: {}' .format(unique_name_size, tracer_var_size, alive_cpp_var_size))
[docs]def to_variable(value, block=None, name=None): """ This function will create a variable from ndarray Args: value(ndarray): the numpy value need to be convert block(fluid.Block|None): which block this variable will be in name(str|None): Name of Varaible return: Variable: The variable created from given numpy Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid with fluid.dygraph.guard(): x = np.ones([2, 2], np.float32) y = fluid.dygraph.to_variable(x) """ if isinstance(value, np.ndarray): assert enabled(), "to_variable could only be called in dygraph mode" if not block: block = framework.default_main_program().current_block() py_var = framework.Variable( block, type=core.VarDesc.VarType.LOD_TENSOR, name=name, shape=value.shape, dtype=value.dtype, stop_gradient=True) var = py_var._ivar.value() tensor = var.get_tensor() tensor.set(value, framework._current_expected_place()) return py_var elif isinstance(value, framework.Variable): return value else: raise TypeError( "to_variable only accepts 'ndarray' and 'Variable' as value's input")