Source code for mindspore.ops.composite.base

# This is the Python adaptation and derivative work of Myia (
# Copyright 2020 Huawei Technologies Co., Ltd
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

"""Basic composite operations."""

from ..._c_expression import EnvInstance_, GradOperation_, HyperMap_, MultitypeFuncGraph_, Tail_, TensorSlice_, \
                             TupleAdd_, TupleSlice_, UnpackCall_, ZipOperation_, ListAppend_
from ...common import dtype as mstype
from ...common.api import ms_function
from .. import functional as F
from .. import operations as P

__all__ = [EnvInstance_, TensorSlice_, TupleAdd_, TupleSlice_, UnpackCall_]

[docs]def add_flags(fn, **flags): """ An interface to add flag for a function. Note: Only supports bool value. Args: fn (Function): Function or cell to add flag. flags (bool): Flags use kwargs. Returns: Function, the fn added flags. Examples: >>> add_flags(net, predit=True) """ # need set the attr and access on c++ if not hasattr(fn, "_mindspore_flags"): fn._mindspore_flags = {} fn._mindspore_flags.update({**flags}) return fn
[docs]def core(fn=None, **flags): """ A decorator to add flag to a function. By default, the function is marked core=True using this decorator to set flag to a graph. Args: fn (Function): Function to add flag. Default: None. flags (dict): The following flags can be set core, which indicates that this is a core function or other flag. Default: None. """ # need set the attr and access on c++ def deco(fn): fn._mindspore_flags = { 'core': True, **flags, } return fn if fn is not None: ret = deco(fn) else: ret = deco return ret
[docs]class GradOperation(GradOperation_): """ An metafuncgraph object which is used to get the gradient of output of a network(function). The GradOperation will convert the network(function) into a back propagation graph. Args: get_all (bool): If True, get all the gradients w.r.t inputs. Default: False. get_by_list (bool): If True, get all the gradients w.r.t Parameter variables. If get_all and get_by_list are both False, get the gradient w.r.t first input. If get_all and get_by_list are both True, get the gradients w.r.t inputs and Parameter variables at the same time in the form of ((grads w.r.t inputs), (grads w.r.t parameters)). Default: False. sens_param (bool): Whether append sensitivity as input. If sens_param is False, a 'ones_like(outputs)' sensitivity will be attached automatically. Default: False. """ def __init__(self, name, get_all=False, get_by_list=False, sens_param=False): self.get_all = get_all self.get_by_list = get_by_list self.sens_param = sens_param GradOperation_.__init__(self, name, get_all, get_by_list, sens_param) self.grad_fn = None self.fn = None def __call__(self, fn, weights=None): grad_ = GradOperation('grad', self.get_all, self.get_by_list, self.sens_param) if self.grad_fn is None or self.fn != fn: if self.get_by_list: @ms_function(obj=fn) def after_grad(*args): return grad_(fn, weights)(*args) else: @ms_function(obj=fn) def after_grad(*args): return grad_(fn)(*args) self.grad_fn = after_grad self.fn = fn return self.grad_fn
grad = GradOperation('grad') grad_all = GradOperation('get_all', get_all=True) grad_by_list = GradOperation('get_by_list', get_by_list=True) grad_with_sens = GradOperation('grad_with_sens', sens_param=True) grad_all_with_sens = GradOperation('grad_all_with_sens', get_all=True, sens_param=True) grad_by_list_with_sens = GradOperation('grad_by_list_with_sens', get_by_list=True, sens_param=True)
[docs]class MultitypeFuncGraph(MultitypeFuncGraph_): """ Generate multiply graph. MultitypeFuncGraph is a class used to generate graphs for function with different type as input. Args: name (str): Operator name. Raises: ValueError: Cannot find matching fn for the given args. Examples: >>> # `add` is a metagraph object which will add two objects according to >>> # input type using ".register" decorator. >>> add = MultitypeFuncGraph('add') """ def __init__(self, name): MultitypeFuncGraph_.__init__(self, name) self.entries = list() def __call__(self, *args): for sig, fn in self.entries: if len(sig) != len(args): continue output = fn(*args) return output raise ValueError("Cannot find fn match given args.")
[docs] def register(self, *type_names): """Register a function for the given type string.""" def deco(fn): self.register_fn(type_names, fn) self.entries.append((type_names, fn)) return fn return deco
[docs]class HyperMap(HyperMap_): """ Hypermap will apply the set operation on input sequences. Which will apply the operations of every elements of the sequence. Args: ops (Union[MultitypeFuncGraph, None]): `ops` is the operation to apply. If `ops` is `None`, the operations should be putted in the first input of the instance. Inputs: - **args** (Tuple[sequence]) - If `ops` is not `None`, all the inputs should be the same length sequences, and each row of the sequences. e.g. If args length is 2, and for `i` in length of each sequence `(args[0][i], args[1][i])` will be the input of the operation. If `ops` is not `None`, the first input is the operation, and the other is inputs. Outputs: sequence, the output will be same type and same length of sequence from input and the value of each element is the result of operation apply each row of element. e.g. `operation(args[0][i], args[1][i])`. """ def __init__(self, ops=None): self.ops = ops if ops: HyperMap_.__init__(self, ops) else: HyperMap_.__init__(self) def __call__(self, *args): func = args[0] count = 0 count_max = 1 args_list = args[1:] if self.ops is not None: func = self.ops args_list = args for item in args_list: if isinstance(item, (tuple, list)): count_max = len(item) break def get_item(x): nonlocal count if isinstance(x, (tuple, list)): return x[count] return x for i in range(count_max): true_args = tuple(map(get_item, args_list)) func(*true_args) count = i + 1 return True
[docs] def register(self, *type_names): """Register a function for the given type string.""" def deco(fn): self.register_fn(type_names, fn) return fn return deco
class _ListAppend(ListAppend_): """ A metafuncgraph class that append one element to list. Args: name (str): The name of the metafuncgraph object. """ def __init__(self, name): ListAppend_.__init__(self, name) def __call__(self, *args): pass _append = _ListAppend("append") class _Tail(Tail_): """ A metafuncgraph class that generates tail elements of the tuple. Args: name (str): The name of the metafuncgraph object. """ def __init__(self, name): Tail_.__init__(self, name) def __call__(self, *args): pass tail = _Tail('tail') class _ZipOperation(ZipOperation_): """Generates a tuple of zip iterations for inputs.""" def __init__(self, name): ZipOperation_.__init__(self, name) def __call__(self, *args): pass zip_operation = _ZipOperation('zip_operation') """`zip_operation` will generate a tuple of zip iterations of inputs.""" env_get = MultitypeFuncGraph("env_get") @env_get.register("EnvType", "Tensor") def _tensor_env_get(env, parameter): """Used to get env.""" return F.env_getitem(env, F.ref_to_embed(parameter), F.zeros_like_tensor(parameter)) _mp_cast_helper = MultitypeFuncGraph('mixed_precision_cast_helper') @_mp_cast_helper.register("TypeType", "Number") @core def _mixed_precision_cast_helper_1(type_, x): """if x is float cast to type.""" # type_ is place holder return x @_mp_cast_helper.register("TypeType", "Tensor") @core def _mixed_precision_cast_helper_2(type_, x): """if x is float cast to type.""" if F.issubclass_(F.dtype(x), mstype.float_): return P.Cast()(x, type_) return x