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未验证 提交 4dbe441c 编写于 作者: C cyberslack_lee 提交者： GitHub
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[xdoctest] reformat example code with google style in No. 250-260 (#56541) 

* test=docs_preview

* test=docs_preview

* test=docs_preview

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* fix

* test=docs_preview

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* fix

* move stmts under imports

---------
Co-authored-by: NSigureMo <sigure.qaq@gmail.com>
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python/paddle/incubate/distributed/fleet/parameter_server/pslib/__init__.py
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...@@ -67,8 +67,8 @@ class PSLib(Fleet): ...@@ -67,8 +67,8 @@ class PSLib(Fleet):
should call init_worker() to initialize global information about worker and connect should call init_worker() to initialize global information about worker and connect
worker with pserver. You should run startup program before init_worker. worker with pserver. You should run startup program before init_worker.
Args: Args:
executor(Executor): The executor to run for init server. executor (Executor): The executor to run for init server.
programs(Program|None): The program that need to run. programs (Program|None): The program that need to run.
""" """
if len(self._main_programs) == 0: if len(self._main_programs) == 0:
...@@ -167,16 +167,24 @@ class PSLib(Fleet): ...@@ -167,16 +167,24 @@ class PSLib(Fleet):
def init_server(self, model_dir=None, **kwargs): def init_server(self, model_dir=None, **kwargs):
""" """
init_server() will be called by user. It will load model from model_dir. Called by user. It will load model from model_dir.
Args: Args:
model_dir(str): load model path, can be local or hdfs/afs path. model_dir(str, optional): Load model path, can be local or hdfs/afs path. Default is None.
kwargs: user-defined attributes, currently support following: kwargs: User-defined attributes, currently support following:
model(int): load model mode.
0 is for load whole model, - model(int): Load model mode.
1 is for load delta model (load diff),
default is 0. 0 is for load whole model,
Example: 1 is for load delta model (load diff).
>>> fleet.init_server("/you/path/to/model", mode = 0) Default is 0.
Examples:
.. code-block:: text
fleet.init_server("/you/path/to/model", mode = 0)
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
if isinstance(self._role_maker, HeterRoleMaker): if isinstance(self._role_maker, HeterRoleMaker):
...@@ -192,8 +200,7 @@ class PSLib(Fleet): ...@@ -192,8 +200,7 @@ class PSLib(Fleet):
def run_server(self): def run_server(self):
""" """
init_pserver(): will be called by user. When a user knows current process is_worker(), he/she Called by user. When a user init server, after that he/she should run run_server() to start.
should call init_pserver() to initialize global information about parameter server
""" """
if self._opt_info: if self._opt_info:
if "fleet_desc" in self._opt_info: if "fleet_desc" in self._opt_info:
...@@ -296,8 +303,8 @@ class PSLib(Fleet): ...@@ -296,8 +303,8 @@ class PSLib(Fleet):
def stop_worker(self): def stop_worker(self):
""" """
stop(): will be called after a user finishes his/her training task. Fleet instance will be Will be called after a user finishes his/her training task. Fleet instance will be
destroyed when stop() is called. destroyed when stop_worker() is called.
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
# all worker should be finalize first # all worker should be finalize first
...@@ -315,14 +322,20 @@ class PSLib(Fleet): ...@@ -315,14 +322,20 @@ class PSLib(Fleet):
def distributed_optimizer(self, optimizer, strategy={}): def distributed_optimizer(self, optimizer, strategy={}):
""" """
distributed_optimizer distributed_optimizer
Args: Args:
optimizer(Optimizer): optimizer optimizer (Optimizer): Optimizer.
strategy(dict): strategy strategy (dict): Strategy.
Examples:
.. code-block:: python
fleet.distributed_optimizer(optimizer)
Returns: Returns:
optimizer(DownpourOptimizer): downpour optimizer optimizer(DownpourOptimizer): Downpour optimizer.
Examples:
.. code-block:: text
fleet.distributed_optimizer(optimizer)
""" """
self._optimizer = DownpourOptimizer(optimizer, strategy) self._optimizer = DownpourOptimizer(optimizer, strategy)
return self._optimizer return self._optimizer
...@@ -337,29 +350,42 @@ class PSLib(Fleet): ...@@ -337,29 +350,42 @@ class PSLib(Fleet):
export_for_deployment=True, export_for_deployment=True,
): ):
""" """
save pserver model called from a worker Save pserver model called from a worker.
Args: Args:
executor(Executor): fluid executor
dirname(str): save model path executor (Executor): Fluid executor.
feeded_var_names(list): default None dirname (str): Save model path.
target_vars(list): default None feeded_var_names (list, optional): Default None.
main_program(Program): default None target_vars (list, optional): Default None.
export_for_deployment(bool): default None main_program (Program, optional): Default None.
export_for_deployment (bool, optional): Default None.
Examples: Examples:
.. code-block:: python
fleet.save_inference_model(dirname="hdfs:/my/path") .. code-block:: text
fleet.save_inference_model(dirname="hdfs:/my/path")
""" """
self._fleet_ptr.save_model(dirname, 0) self._fleet_ptr.save_model(dirname, 0)
def print_table_stat(self, table_id, pass_id, threshold): def print_table_stat(self, table_id, pass_id, threshold):
""" """
print stat info of table_id, Print stat info of table_id, format: tableid, feasign size, mf size.
format: tableid, feasign size, mf size
Args: Args:
table_id(int): the id of table
Example: table_id (int): The id of table.
.. code-block:: python pass_id (int): The id of pass.
fleet.print_table_stat(0) threshold (float): The threshold of print.
Examples:
.. code-block:: text
fleet.print_table_stat(0)
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
if self._role_maker.is_first_worker(): if self._role_maker.is_first_worker():
...@@ -368,13 +394,19 @@ class PSLib(Fleet): ...@@ -368,13 +394,19 @@ class PSLib(Fleet):
def set_file_num_one_shard(self, table_id, file_num): def set_file_num_one_shard(self, table_id, file_num):
""" """
set file_num in one shard Set file_num in one shard.
Args: Args:
table_id(int): the id of table
file_num(int): file num in one shard table_id (int): The id of table.
Example: file_num (int): File num in one shard.
.. code-block:: python
fleet.set_file_num_one_shard(0, 5) Examples:
.. code-block:: text
fleet.set_file_num_one_shard(0, 5)
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
if self._role_maker.is_first_worker(): if self._role_maker.is_first_worker():
...@@ -383,20 +415,28 @@ class PSLib(Fleet): ...@@ -383,20 +415,28 @@ class PSLib(Fleet):
def save_persistables(self, executor, dirname, main_program=None, **kwargs): def save_persistables(self, executor, dirname, main_program=None, **kwargs):
""" """
save presistable parameters, Save presistable parameters,
when using fleet, it will save sparse and dense feature when using fleet, it will save sparse and dense feature.
Args: Args:
executor(Executor): fluid executor
dirname(str): save path. It can be hdfs/afs path or local path executor (Executor): Fluid executor.
main_program(Program): fluid program, default None dirname (str): Save path. It can be hdfs/afs path or local path.
kwargs: use define property, current support following main_program (Program, optional): Fluid program, default None.
mode(int): 0 means save all pserver model, kwargs: Use define property, current support following
1 means save delta pserver model (save diff),
2 means save xbox base, - mode (int):
3 means save batch model. 0 means save all pserver model,
Example: 1 means save delta pserver model (save diff),
.. code-block:: python 2 means save xbox base,
fleet.save_persistables(dirname="/you/path/to/model", mode = 0) 3 means save batch model.
Examples:
.. code-block:: test
fleet.save_persistables(dirname="/you/path/to/model", mode = 0)
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
self._fleet_ptr.client_flush() self._fleet_ptr.client_flush()
...@@ -409,23 +449,28 @@ class PSLib(Fleet): ...@@ -409,23 +449,28 @@ class PSLib(Fleet):
self, executor, dirname, whitelist_path, main_program=None, **kwargs self, executor, dirname, whitelist_path, main_program=None, **kwargs
): ):
""" """
save whitelist, mode is consistent with fleet.save_persistables, Save whitelist, mode is consistent with fleet.save_persistables,
when using fleet, it will save sparse and dense feature when using fleet, it will save sparse and dense feature.
Args: Args:
executor(Executor): fluid executor
dirname(str): save path. It can be hdfs/afs path or local path
main_program(Program): fluid program, default None
kwargs: use define property, current support following
mode(int): 0 means save all pserver model,
1 means save delta pserver model (save diff),
2 means save xbox base,
3 means save batch model.
Example: executor (Executor): Fluid executor.
.. code-block:: python dirname (str): save path. It can be hdfs/afs path or local path.
whitelist_path (str): whitelist path. It can be hdfs/afs path or local path.
main_program (Program, optional): fluid program, default None.
kwargs: Use define property, current support following
fleet.save_persistables(dirname="/you/path/to/model", mode = 0) - mode (int):
0 means save all pserver model,
1 means save delta pserver model (save diff),
2 means save xbox base,
3 means save batch model.
Examples:
.. code-block:: text
fleet.save_persistables(dirname="/you/path/to/model", mode = 0)
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
...@@ -440,18 +485,23 @@ class PSLib(Fleet): ...@@ -440,18 +485,23 @@ class PSLib(Fleet):
def save_multi_table_one_path(self, table_ids, model_dir, **kwargs): def save_multi_table_one_path(self, table_ids, model_dir, **kwargs):
""" """
save pslib multi sparse table in one path. Save pslib multi sparse table in one path.
Args: Args:
table_ids(list): table ids
model_dir(str): if you use hdfs, model_dir should starts with table_ids (list): Table ids.
'hdfs:', otherwise means local dir model_dir (str): If you use hdfs, model_dir should starts with 'hdfs:', otherwise means local dir.
kwargs(dict): user-defined properties. kwargs (dict): User-defined properties.
mode(int): the modes illustrated above, default 0
prefix(str): the parts to save can have prefix, - mode (int): The modes illustrated above, default 0.
for example, part-prefix-000-00000 - prefix (str): the parts to save can have prefix, for example, part-prefix-000-00000.
Examples: Examples:
.. code-block:: python
fleet.save_multi_table_one_path("[0, 1]", "afs:/user/path/") .. code-block:: text
fleet.save_multi_table_one_path("[0, 1]", "afs:/user/path/")
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
...@@ -463,21 +513,30 @@ class PSLib(Fleet): ...@@ -463,21 +513,30 @@ class PSLib(Fleet):
def save_cache_model(self, executor, dirname, main_program=None, **kwargs): def save_cache_model(self, executor, dirname, main_program=None, **kwargs):
""" """
save sparse cache table, Save sparse cache table,
when using fleet, it will save sparse cache table when using fleet, it will save sparse cache table.
Args: Args:
executor(Executor): fluid executor
dirname(str): save path. It can be hdfs/afs path or local path executor (Executor): Fluid executor.
main_program(Program): fluid program, default None dirname (str): Save path. It can be hdfs/afs path or local path.
kwargs: use define property, current support following main_program (Program, optional): Fluid program, default None.
mode(int): define for feature extension in the future, kwargs: Use define property, current support following
currently no use, will pass a default value 0
table_id(int): which table to save cache, default is 0 - mode (int): Define for feature extension in the future,
currently no use, will pass a default value 0.
- table_id (int): Which table to save cache, default is 0.
Returns: Returns:
feasign_num(int): cache feasign num
Example: feasign_num (int): cache feasign num.
.. code-block:: python
fleet.save_cache_model(None, dirname="/you/path/to/model", mode = 0) Examples:
.. code-block:: text
fleet.save_cache_model(None, dirname="/you/path/to/model", mode = 0)
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
table_id = kwargs.get("table_id", 0) table_id = kwargs.get("table_id", 0)
...@@ -506,10 +565,15 @@ class PSLib(Fleet): ...@@ -506,10 +565,15 @@ class PSLib(Fleet):
def shrink_sparse_table(self): def shrink_sparse_table(self):
""" """
shrink cvm of all sparse embedding in pserver, the decay rate Shrink cvm of all sparse embedding in pserver, the decay rate
is defined as "show_click_decay_rate" in fleet_desc.prototxt is defined as "show_click_decay_rate" in fleet_desc.prototxt.
Example:
>>> fleet.shrink_sparse_table() Examples:
.. code-block:: text
fleet.shrink_sparse_table()
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
if self._role_maker.is_first_worker(): if self._role_maker.is_first_worker():
...@@ -523,18 +587,22 @@ class PSLib(Fleet): ...@@ -523,18 +587,22 @@ class PSLib(Fleet):
def shrink_dense_table(self, decay, emb_dim=11, scope=None, table_id=None): def shrink_dense_table(self, decay, emb_dim=11, scope=None, table_id=None):
""" """
shrink batch_sum in pserver by multiplying by decay Shrink batch_sum in pserver by multiplying by decay.
Args: Args:
decay(float): the decay rate, usually range in (0, 1) decay (float): The decay rate, usually range in (0, 1).
emb_dim(int): one element's length in datanorm layer emb_dim (int, optional): One element's length in datanorm layer. Default is 11.
scope(Scope): Scope object, default is fluid.global_scope() scope (Scope, optional): Scope object, default is fluid.global_scope(). Default is None.
table_id(int): table id of shrinking dense table. None means shrink all, table_id (int, optional): Table id of shrinking dense table. None means shrink all,
you should specify it when using multiple scopes, you should specify it when using multiple scopes, default is None.
default is None.
Example: Examples:
>>> fleet.shrink_dense_table(0.98, 11, myscope1, 1)
>>> fleet.shrink_dense_table(0.98, 11, myscope1, 2) .. code-block:: text
>>> fleet.shrink_dense_table(0.98, 11, myscope2, 3)
fleet.shrink_dense_table(0.98, 11, myscope1, 1)
fleet.shrink_dense_table(0.98, 11, myscope1, 2)
fleet.shrink_dense_table(0.98, 11, myscope2, 3)
""" """
if scope is None: if scope is None:
scope = paddle.static.global_scope() scope = paddle.static.global_scope()
...@@ -559,12 +627,17 @@ class PSLib(Fleet): ...@@ -559,12 +627,17 @@ class PSLib(Fleet):
def clear_one_table(self, table_id): def clear_one_table(self, table_id):
""" """
clear_one_table() will be called by user. It will clear one table. This function will be called by user. It will clear one table.
Args: Args:
table_id(int): table id
table_id (int): Table id.
Examples: Examples:
.. code-block:: python
fleet.clear_one_table(0) .. code-block:: text
fleet.clear_one_table(0)
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
if self._role_maker.is_first_worker(): if self._role_maker.is_first_worker():
...@@ -573,10 +646,13 @@ class PSLib(Fleet): ...@@ -573,10 +646,13 @@ class PSLib(Fleet):
def clear_model(self): def clear_model(self):
""" """
clear_model() will be called by user. It will clear sparse model. This function will be called by user. It will clear sparse model.
Examples: Examples:
.. code-block:: python
fleet.clear_model() .. code-block:: text
fleet.clear_model()
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
if self._role_maker.is_first_worker(): if self._role_maker.is_first_worker():
...@@ -585,40 +661,39 @@ class PSLib(Fleet): ...@@ -585,40 +661,39 @@ class PSLib(Fleet):
def load_pslib_whitelist(self, table_id, model_path, **kwargs): def load_pslib_whitelist(self, table_id, model_path, **kwargs):
""" """
load pslib model for one table with whitelist Load pslib model for one table with whitelist.
Args: Args:
table_id(int): load table id table_id (int): Load table id.
model_path(str): load model path, can be local or hdfs/afs path model_path (str): Load model path, can be local or hdfs/afs path.
kwargs(dict): user defined params, currently support following: kwargs (dict): User defined params, currently support following:
only for load pslib model for one table:
mode(int): load model mode. 0 is for load whole model, 1 is - only for load pslib model for one table:
for load delta model (load diff), default is 0. mode (int): load model mode. 0 is for load whole model, 1 is for load delta model (load diff), default is 0.
only for load params from paddle model: - only for load params from paddle model:
scope(Scope): Scope object scope (Scope): Scope object.
model_proto_file(str): path of program desc proto binary model_proto_file (str): Path of program desc proto binary file, can be local or hdfs/afs file.
file, can be local or hdfs/afs file var_names (list): Var name list.
var_names(list): var name list load_combine (bool): Load from a file or split param files, default False.
load_combine(bool): load from a file or split param files
default False.
Examples: Examples:
.. code-block:: python
# load pslib model for one table .. code-block:: text
fleet.load_one_table(0, "hdfs:/my_fleet_model/20190714/0/")
fleet.load_one_table(1, "hdfs:/xx/xxx", mode = 0) # load pslib model for one table
fleet.load_one_table(0, "hdfs:/my_fleet_model/20190714/0/")
fleet.load_one_table(1, "hdfs:/xx/xxx", mode = 0)
# load params from paddle model # load params from paddle model
fleet.load_one_table(2, "hdfs:/my_paddle_model/", fleet.load_one_table(2, "hdfs:/my_paddle_model/",
scope = my_scope, scope = my_scope,
model_proto_file = "./my_program.bin", model_proto_file = "./my_program.bin",
load_combine = False) load_combine = False)
# below is how to save proto binary file # below is how to save proto binary file
with open("my_program.bin", "wb") as fout: with open("my_program.bin", "wb") as fout:
my_program = fluid.default_main_program() my_program = fluid.default_main_program()
fout.write(my_program.desc.serialize_to_string()) fout.write(my_program.desc.serialize_to_string())
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
...@@ -631,35 +706,39 @@ class PSLib(Fleet): ...@@ -631,35 +706,39 @@ class PSLib(Fleet):
def load_one_table(self, table_id, model_path, **kwargs): def load_one_table(self, table_id, model_path, **kwargs):
""" """
load pslib model for one table or load params from paddle model Load pslib model for one table or load params from paddle model.
Args: Args:
table_id(int): load table id
model_path(str): load model path, can be local or hdfs/afs path table_id (int): Load table id.
kwargs(dict): user defined params, currently support following: model_path (str): Load model path, can be local or hdfs/afs path.
only for load pslib model for one table: kwargs (dict): user defined params, currently support following:
mode(int): load model mode. 0 is for load whole model, 1 is
for load delta model (load diff), default is 0. - only for load pslib model for one table:
only for load params from paddle model: mode(int): load model mode. 0 is for load whole model, 1 is for load delta model (load diff), default is 0.
scope(Scope): Scope object - only for load params from paddle model:
model_proto_file(str): path of program desc proto binary scope(Scope): Scope object.
file, can be local or hdfs/afs file model_proto_file(str): Path of program desc proto binary file, can be local or hdfs/afs file.
var_names(list): var name list var_names(list): var name list.
load_combine(bool): load from a file or split param files load_combine(bool): load from a file or split param files, default False.
default False.
Examples: Examples:
.. code-block:: python
# load pslib model for one table .. code-block:: text
fleet.load_one_table(0, "hdfs:/my_fleet_model/20190714/0/")
fleet.load_one_table(1, "hdfs:/xx/xxx", mode = 0) # load pslib model for one table
# load params from paddle model fleet.load_one_table(0, "hdfs:/my_fleet_model/20190714/0/")
fleet.load_one_table(2, "hdfs:/my_paddle_model/", fleet.load_one_table(1, "hdfs:/xx/xxx", mode = 0)
scope = my_scope, # load params from paddle model
model_proto_file = "./my_program.bin", fleet.load_one_table(2, "hdfs:/my_paddle_model/",
load_combine = False) scope = my_scope,
# below is how to save proto binary file model_proto_file = "./my_program.bin",
with open("my_program.bin", "wb") as fout: load_combine = False)
my_program = fluid.default_main_program() # below is how to save proto binary file
fout.write(my_program.desc.serialize_to_string()) with open("my_program.bin", "wb") as fout:
my_program = fluid.default_main_program()
fout.write(my_program.desc.serialize_to_string())
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
...@@ -691,15 +770,16 @@ class PSLib(Fleet): ...@@ -691,15 +770,16 @@ class PSLib(Fleet):
load_combine=False, load_combine=False,
): ):
""" """
load params from paddle model, and push params to pserver Load params from paddle model, and push params to pserver.
Args: Args:
scope(Scope): Scope object scope (Scope): Scope object.
table_id(int): the id of table to load table_id (int): The id of table to load.
model_path(str): path of paddle model, can be local or hdfs/afs file model_path (str): Path of paddle model, can be local or hdfs/afs file.
model_proto_file(str): path of program desc proto binary file, model_proto_file (str): Path of program desc proto binary file, can be local or hdfs/afs file.
can be local or hdfs/afs file var_names (list, optional): Load var names. Default is None.
var_names(list): load var names load_combine (bool, optional): Load from a file or split param files. Default is False.
load_combine(bool): load from a file or split param files
""" """
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
if self._role_maker.is_first_worker(): if self._role_maker.is_first_worker():
...@@ -800,14 +880,18 @@ class PSLib(Fleet): ...@@ -800,14 +880,18 @@ class PSLib(Fleet):
usually for online predict) usually for online predict)
3: load batch model (do some statistic works in checkpoint, such as 3: load batch model (do some statistic works in checkpoint, such as
calculate unseen days of each feasign) calculate unseen days of each feasign)
Args: Args:
model_dir(str): if you use hdfs, model_dir should starts with model_dir (str, optional): If you use hdfs, model_dir should starts with
'hdfs:', otherwise means local dir 'hdfs:', otherwise means local dir. Default is None.
kwargs(dict): user-defined properties. kwargs (dict): user-defined properties.
mode(int): the modes illustrated above, default 0
- mode (int): The modes illustrated above, default 0.
Examples: Examples:
.. code-block:: python .. code-block:: text
fleet.load_model("afs:/user/path/")
fleet.load_model("afs:/user/path/")
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
self._role_maker._barrier_worker() self._role_maker._barrier_worker()
...@@ -818,14 +902,19 @@ class PSLib(Fleet): ...@@ -818,14 +902,19 @@ class PSLib(Fleet):
def save_model(self, model_dir=None, **kwargs): def save_model(self, model_dir=None, **kwargs):
""" """
save pslib model, the modes are same with load model. save pslib model, the modes are same with load model.
Args: Args:
model_dir(str): if you use hdfs, model_dir should starts with model_dir (str, optional): If you use hdfs, model_dir should starts with
'hdfs:', otherwise means local dir 'hdfs:', otherwise means local dir. Default is None.
kwargs(dict): user-defined properties. kwargs (dict): user-defined properties.
mode(int): the modes illustrated above, default 0
- mode (int): The modes illustrated above, default 0.
Examples: Examples:
.. code-block:: python .. code-block:: text
fleet.save_model("afs:/user/path/")
fleet.save_model("afs:/user/path/")
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
prefix = kwargs.get("prefix", None) prefix = kwargs.get("prefix", None)
...@@ -836,18 +925,21 @@ class PSLib(Fleet): ...@@ -836,18 +925,21 @@ class PSLib(Fleet):
def save_one_table(self, table_id, model_dir, **kwargs): def save_one_table(self, table_id, model_dir, **kwargs):
""" """
save pslib model's one table, the modes are same with load model. Save pslib model's one table, the modes are same with load model.
Args: Args:
table_id(int): table id table_id (int): Table id.
model_dir(str): if you use hdfs, model_dir should starts with model_dir (str): if you use hdfs, model_dir should starts with
'hdfs:', otherwise means local dir 'hdfs:', otherwise means local dir.
kwargs(dict): user-defined properties. kwargs (dict): user-defined properties.
mode(int): the modes illustrated above, default 0
prefix(str): the parts to save can have prefix, - mode (int): the modes illustrated above, default 0.
for example, part-prefix-000-00000 - prefix (str): the parts to save can have prefix, for example, part-prefix-000-00000.
Examples: Examples:
.. code-block:: python .. code-block:: text
fleet.save_one_table("afs:/user/path/")
fleet.save_one_table("afs:/user/path/")
""" """
mode = kwargs.get("mode", 0) mode = kwargs.get("mode", 0)
prefix = kwargs.get("prefix", None) prefix = kwargs.get("prefix", None)
...@@ -890,15 +982,17 @@ def _prepare_params( ...@@ -890,15 +982,17 @@ def _prepare_params(
dtype='float32', dtype='float32',
): ):
""" """
preprocess params, this interface is not for users. Preprocess params, this interface is not for users.
Args: Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable input (Variable|list of Variable): Input is a Tensor<int64> Variable.
size(list of int): the embedding dim size (list of int): The embedding dim.
is_sparse(bool): whether input is sparse ids is_sparse (bool, optional): Whether input is sparse ids. Default is False.
is_distributed(bool): whether in distributed mode is_distributed (bool, optional): Whether in distributed mode. Default is False.
padding_idx(int): padding idx of input padding_idx (int, optional): Padding idx of input. Default is None.
param_attr(ParamAttr): To specify the weight parameter property param_attr (ParamAttr, optional): To specify the weight parameter property. Default is None.
dtype(str): data type of output dtype (str, optional): Data type of output. Default is 'float32'.
""" """
if param_attr is None: if param_attr is None:
raise ValueError("param_attr must be set") raise ValueError("param_attr must be set")
...@@ -953,15 +1047,16 @@ def _fleet_embedding( ...@@ -953,15 +1047,16 @@ def _fleet_embedding(
dtype='float32', dtype='float32',
): ):
""" """
add fleet embedding, this interface is not for users. Add fleet embedding, this interface is not for users.
Args: Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable input (Variable|list of Variable): Input is a Tensor<int64> Variable.
size(list of int): the embedding dim size (list[int]): The embedding dim.
is_sparse(bool): whether input is sparse ids is_sparse (bool, optional): Whether input is sparse ids. Default is False.
is_distributed(bool): whether in distributed mode is_distributed (bool, optional): Whether in distributed mode. Default is False.
padding_idx(int): padding idx of input padding_idx (int, optional): Padding idx of input. Default is None.
param_attr(ParamAttr): To specify the weight parameter property param_attr (ParamAttr, optional): To specify the weight parameter property. Default is None.
dtype(str): data type of output dtype (str, optional): Data type of output. Default is 'float32'.
""" """
def _pull_sparse( def _pull_sparse(
...@@ -1041,15 +1136,16 @@ def _fleet_embedding_v2( ...@@ -1041,15 +1136,16 @@ def _fleet_embedding_v2(
dtype='float32', dtype='float32',
): ):
""" """
add fleet embedding v2, this interface is not for users. Add fleet embedding v2, this interface is not for users.
Args: Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable input (Variable|list of Variable): Input is a Tensor<int64> Variable.
size(list of int): the embedding dim size (list[int]): The embedding dim.
is_sparse(bool): whether input is sparse ids is_sparse (bool, optional): Whether input is sparse ids. Default is False.
is_distributed(bool): whether in distributed mode is_distributed (bool, optional): Whether in distributed mode. Default is False.
padding_idx(int): padding idx of input padding_idx (int, optional): Padding idx of input. Default is None.
param_attr(ParamAttr): To specify the weight parameter property param_attr (ParamAttr, optional): To specify the weight parameter property. Default is None.
dtype(str): data type of output dtype (str, optional): Data type of output. Default is 'float32'.
""" """
def _pull_sparse_v2( def _pull_sparse_v2(
...@@ -1120,16 +1216,19 @@ def _fleet_embedding_v2( ...@@ -1120,16 +1216,19 @@ def _fleet_embedding_v2(
class fleet_embedding: class fleet_embedding:
""" """
fleet embedding class, it is used as a wrapper Fleet embedding class, it is used as a wrapper.
Example:
.. code-block:: python Examples:
with fleet_embedding(click_name=label.name):
emb = paddle.static.nn.embedding( .. code-block:: text
input=var,
size=[-1, 11], with fleet_embedding(click_name=label.name):
is_sparse=True, emb = paddle.static.nn.embedding(
is_distributed=True, input=var,
param_attr=fluid.ParamAttr(name="embedding")) size=[-1, 11],
is_sparse=True,
is_distributed=True,
param_attr=fluid.ParamAttr(name="embedding"))
""" """
def __init__(self, click_name, scale_sparse_grad=True): def __init__(self, click_name, scale_sparse_grad=True):
...@@ -1165,9 +1264,11 @@ class DownpourOptimizer(DistributedOptimizer): ...@@ -1165,9 +1264,11 @@ class DownpourOptimizer(DistributedOptimizer):
run distributed training. The optimized information will be stored in run distributed training. The optimized information will be stored in
Fleet() instance who holds the global information about current distributed Fleet() instance who holds the global information about current distributed
training. training.
Args: Args:
optimizer(Optimizer): subclass of Optimizer. optimizer(Optimizer): subclass of Optimizer.
strategy(any): config for DownpourOptimizer. strategy(any): config for DownpourOptimizer.
Returns: Returns:
None None
""" """
...@@ -1270,22 +1371,24 @@ class DownpourOptimizer(DistributedOptimizer): ...@@ -1270,22 +1371,24 @@ class DownpourOptimizer(DistributedOptimizer):
program_mode="all_reduce", program_mode="all_reduce",
): ):
""" """
minimize a program through loss, loss can be a list in DistributedOptimizer. Minimize a program through loss, loss can be a list in DistributedOptimizer.
Note that in parameter server mode, a worker will not get anything about optimize_os Note that in parameter server mode, a worker will not get anything about optimize_os
Because optimizer algorithms run on pserver side. We will make this usable in pserver Because optimizer algorithms run on pserver side. We will make this usable in pserver
process, but currently the optimization part is written into Fleet(). A user does not process, but currently the optimization part is written into Fleet(). A user does not
need to care about how to startup a pserver node. need to care about how to startup a pserver node.
Args: Args:
losses (Variable|Variable List): loss variable or loss variable list to run optimization. losses (Variable|Variable List): Loss variable or loss variable list to run optimization.
scopes (Scope| Scope List): scope instance. scopes (Scope|Scope List, Optional): Scope instance. Default is None.
startup_programs (Program|Program List): startup_program for initializing parameters startup_programs (Program|Program List, Optional): Startup_program for initializing parameters
in `parameter_list`. in `parameter_list`. Default is None.
parameter_list (list): list of Variables to update. parameter_list (list, Optional): List of Variables to update. Default is None.
no_grad_set (set|None): set of Variables should be ignored. no_grad_set (set, Optional): Set of Variables should be ignored. Default is None.
program_mode (str|"all_reduce"): grad action for grogram when use_ps_gpu. program_mode (str, Optional): Grad action for grogram when use_ps_gpu. Default is "all_reduce".
Returns: Returns:
tuple: (optimize_ops, params_grads) which are, list of operators appended; tuple: (optimize_ops, params_grads) which are, list of operators appended;
and list of (param, grad) Variables pair for optimization. and list of (param, grad) Variables pair for optimization.
""" """
if not isinstance(losses, list): if not isinstance(losses, list):
......
python/paddle/incubate/distributed/models/moe/grad_clip.py
浏览文件 @ 4dbe441c
...@@ -22,7 +22,7 @@ from paddle.nn.clip import ClipGradBase, _squared_l2_norm ...@@ -22,7 +22,7 @@ from paddle.nn.clip import ClipGradBase, _squared_l2_norm
class ClipGradForMOEByGlobalNorm(ClipGradBase): class ClipGradForMOEByGlobalNorm(ClipGradBase):
r""" r"""
The Algrithm is the same as paddle.nn.ClipGradByGlobalNorm The Algorithm is the same as paddle.nn.ClipGradByGlobalNorm
Given a list of Tensor :math:`t\_list` , calculate the global norm for the elements of all tensors in Given a list of Tensor :math:`t\_list` , calculate the global norm for the elements of all tensors in
:math:`t\_list` , and limit it to ``clip_norm`` . :math:`t\_list` , and limit it to ``clip_norm`` .
...@@ -50,7 +50,7 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase): ...@@ -50,7 +50,7 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase):
Note: Note:
``need_clip`` of ``ClipGradyGlobalNorm`` HAS BEEN DEPRECATED since 2.0. ``need_clip`` of ``ClipGradyGlobalNorm`` HAS BEEN DEPRECATED since 2.0.
Please use ``need_clip`` in ``ParamAttr`` to speficiy the clip scope. Please use ``need_clip`` in ``ParamAttr`` to specify the clip scope.
Reference: Reference:
https://github.com/laekov/fastmoe/blob/master/examples/megatron/clip-grad-v2.2.patch https://github.com/laekov/fastmoe/blob/master/examples/megatron/clip-grad-v2.2.patch
...@@ -64,22 +64,22 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase): ...@@ -64,22 +64,22 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase):
group_name (str, optional): The group name for this clip. Default value is ``default_moe_group``. group_name (str, optional): The group name for this clip. Default value is ``default_moe_group``.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32') >>> x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
linear = paddle.nn.Linear(in_features=10, out_features=10, >>> linear = paddle.nn.Linear(in_features=10, out_features=10,
weight_attr=paddle.ParamAttr(need_clip=True), ... weight_attr=paddle.ParamAttr(need_clip=True),
bias_attr=paddle.ParamAttr(need_clip=False)) ... bias_attr=paddle.ParamAttr(need_clip=False))
out = linear(x) >>> out = linear(x)
loss = paddle.mean(out) >>> loss = paddle.mean(out)
loss.backward() >>> loss.backward()
is_expert_func = lambda param: "expert_" in param.name >>> clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0) # Cause paddle.nn hasn't this interface, so we use ClipGradByGlobalNorm here.
clip = paddle.nn.ClipGradForMOEByGlobalNorm(clip_norm=1.0,is_expert_func, None) >>> sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip)
sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip) >>> sdg.step()
sdg.step()
""" """
def __init__( def __init__(
...@@ -124,7 +124,7 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase): ...@@ -124,7 +124,7 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase):
else: else:
sum_square_list.append(sum_square) sum_square_list.append(sum_square)
# all parameters have been filterd out # all parameters have been filtered out
if ( if (
len(sum_square_list) len(sum_square_list)
+ len(sum_square_list_fp16) + len(sum_square_list_fp16)
......
python/paddle/incubate/distributed/models/moe/moe_layer.py
浏览文件 @ 4dbe441c
...@@ -263,63 +263,68 @@ def prepare_forward(gate, num_expert, world_size, moe_group): ...@@ -263,63 +263,68 @@ def prepare_forward(gate, num_expert, world_size, moe_group):
class MoELayer(nn.Layer): class MoELayer(nn.Layer):
"""MoE Layer """MoE Layer
Args: Args:
d_model: (int) model dimention d_model (int): Model dimention.
experts: (nn.LayerList) expert networks list experts (nn.LayerList): Expert networks list.
gate: (dict|NaiveGate|SwitchGate|NaiveGate): gate (dict|NaiveGate|SwitchGate|NaiveGate):
if gate is a dict:
gate is a gate network config, containing 2 keys: - If gate is a dict:
`type`(str) value can be: "naive", "gshard", "switch" or None, default is "gshard" gate is a gate network config, containing 2 keys:
`top_k`(int) default value is 2 `type` (str) value can be: "naive", "gshard", "switch" or None, default is "gshard".
else gate is an instance of NaiveGate|SwitchGate|NaiveGate: `top_k` (int) Default value is 2.
else gate is an instance of NaiveGate|SwitchGate|NaiveGate:
moe_group: moe group for experts communication
mp_group: mp group for mp commutication moe_group: moe group for experts communication.
recompute_interval(int, optional): whether to use recompute, default 0, means to disable recompute. mp_group: mp group for mp communication.
recompute_ctx(dict, optional): the context for recompute, if recompute_interval > 1, recompute_ctx must be given. recompute_interval (int, optional): Whether to use recompute, default 0, means to disable recompute.
recompute_ctx (dict, optional): The context for recompute, if recompute_interval > 1, recompute_ctx must be given.
Examples: Examples:
.. code-block:: python .. code-block:: python
from paddle.nn import layer, LayerList
from paddle.distributed.moe import MoElayer
from paddle.distributed.collective import Group
from paddle.distributed import fleet
moe_group = Group(fleet.worker_index(),
0,
list(range(fleet.worker_num())))
mp_group = None
num_experts=8
dim_feedforward=512
d_model=8
top_k=2
class ExpertLayer(Layer):
def __init__(self, d_model, d_hidden, name=None,rank=0, windex = 0, num_expert=1):
super().__init__()
self.htoh4 = nn.Linear(d_model, d_hidden)
self.h4toh = nn.Linear(d_hidden, d_model)
def forward(self, x):
x = self.htoh4(x)
x = self.h4toh(x)
return x
gate_config = { >>> # doctest: +SKIP('Until Distributed move successfully, just skip it')
"type": "gshard", >>> from paddle.nn import layer, LayerList
"top_k": top_k, >>> from paddle.distributed.moe import MoElayer
} >>> from paddle.distributed.collective import Group
>>> from paddle.distributed import fleet
experts_list = LayerList()
for expi in range(num_experts): >>> moe_group = Group(fleet.worker_index(),
exp_layer = ExpertLayer(d_model, dim_feedforward // top_k, windex=expi, num_expert=num_experts) ... 0,
experts_list.append(exp_layer) ... list(range(fleet.worker_num())))
>>> mp_group = None
moeLayer = MoELayer(d_model = d_model,
experts=experts_list, >>> num_experts=8
gate=gate_config, >>> dim_feedforward=512
moe_group=moe_group, >>> d_model=8
mp_group=mp_group, >>> top_k=2
recompute_interval=0)
>>> class ExpertLayer(Layer):
... def __init__(self, d_model, d_hidden, name=None,rank=0, windex = 0, num_expert=1):
... super().__init__()
... self.htoh4 = nn.Linear(d_model, d_hidden)
... self.h4toh = nn.Linear(d_hidden, d_model)
... def forward(self, x):
... x = self.htoh4(x)
... x = self.h4toh(x)
... return x
>>> gate_config = {
... "type": "gshard",
... "top_k": top_k,
... }
>>> experts_list = LayerList()
>>> for expi in range(num_experts):
... exp_layer = ExpertLayer(d_model, dim_feedforward // top_k, windex=expi, num_expert=num_experts)
... experts_list.append(exp_layer)
>>> moeLayer = MoELayer(d_model = d_model,
... experts=experts_list,
... gate=gate_config,
... moe_group=moe_group,
... mp_group=mp_group,
... recompute_interval=0)
""" """
......
python/paddle/incubate/layers/nn.py
浏览文件 @ 4dbe441c
...@@ -54,37 +54,40 @@ def fused_embedding_seq_pool( ...@@ -54,37 +54,40 @@ def fused_embedding_seq_pool(
size (tuple|list): The shape of the lookup_table parameter. It should size (tuple|list): The shape of the lookup_table parameter. It should
have two elements which indicate the size of the dictionary of have two elements which indicate the size of the dictionary of
embedding and the size of each embedding vector respectively. embedding and the size of each embedding vector respectively.
is_sparse (bool): The flag indicating whether to use sparse update. is_sparse (bool, optional): The flag indicating whether to use sparse update.
Default: False. Default: False.
padding_idx (int|long|None): It will output all-zero padding data whenever padding_idx (int|long|None, optional): It will output all-zero padding data whenever
lookup encounters :math:`padding\_idx` in Ids. If set :attr:`None`, it makes lookup encounters :math:`padding\_idx` in Ids. If set :attr:`None`, it makes
no effect to output. If :math:`padding\_idx < 0`, the :math:`padding\_idx` no effect to output. If :math:`padding\_idx < 0`, the :math:`padding\_idx`
will automatically be converted to :math:`size[0] + padding\_idx` to use. will automatically be converted to :math:`size[0] + padding\_idx` to use.
Default: None. Default: None.
combiner (str): The pooling type of sequence_pool, and only support `sum`. combiner (str, optional): The pooling type of sequence_pool, and only support `sum`.
Default: sum. Default: sum.
param_attr (ParamAttr): Parameters for this layer. param_attr (ParamAttr, optional): Parameters for this layer. Default: None.
dtype (np.dtype|core.VarDesc.VarType|str): The dtype refers to the data type of output dtype (np.dtype|core.VarDesc.VarType|str, optional): The dtype refers to the data type of output
tensor. It can be float32, float_16, int etc. tensor. It can be float32, float_16, int etc. Default: float32.
Returns: Returns:
The Tensor of sequence pooling. The Tensor of sequence pooling.
Examples: Examples:
.. code-block:: python .. code-block:: python
import numpy as np
import paddle.fluid as fluid >>> import numpy as np
import paddle >>> import paddle
paddle.enable_static() >>> paddle.enable_static()
dict_size = 20 >>> dict_size = 20
data_t = paddle.static.data( >>> data_t = paddle.static.data(
name='word', shape=[-1, 1], dtype='int64', lod_level=1) ... name='word', shape=[-1, 1], dtype='int64', lod_level=1)
padding_idx = np.random.randint(1, 10) >>> padding_idx = np.random.randint(1, 10)
out = paddle.incubate.layers.fused_embedding_seq_pool( >>> out = paddle.incubate.layers.fused_embedding_seq_pool(
input=data_t, ... input=data_t,
size=[dict_size, 32], ... size=[dict_size, 32],
param_attr='w', ... param_attr='w',
padding_idx=padding_idx, ... padding_idx=padding_idx,
is_sparse=False) ... is_sparse=False)
""" """
helper = LayerHelper('fused_embedding_seq_pool', **locals()) helper = LayerHelper('fused_embedding_seq_pool', **locals())
w = helper.create_parameter( w = helper.create_parameter(
...@@ -130,27 +133,25 @@ def fused_seqpool_cvm( ...@@ -130,27 +133,25 @@ def fused_seqpool_cvm(
cvm_offset(int, optional): cvm offset. Default: 2, which means cvm contains show, click. cvm_offset(int, optional): cvm offset. Default: 2, which means cvm contains show, click.
Returns: Returns:
Tensor : The tensor storing sequence pool and cvm Tensor : The tensor storing sequence pool and cvm of input.
of input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
import paddle.fluid as fluid >>> paddle.enable_static()
paddle.enable_static()
data = paddle.static.data(name='x', shape=[-1, 1], dtype='int64', lod_level=1) >>> data = paddle.static.data(name='x', shape=[-1, 1], dtype='int64', lod_level=1)
data2 = paddle.static.data(name='y', shape=[-1, 1], dtype='int64', lod_level=1) >>> data2 = paddle.static.data(name='y', shape=[-1, 1], dtype='int64', lod_level=1)
inputs = [data, data2] >>> inputs = [data, data2]
embs = paddle.incubate.layers.nn._pull_box_sparse(input=inputs, size=11, is_distributed=True, is_sparse=True) >>> embs = paddle.incubate.layers.nn._pull_box_sparse(input=inputs, size=11, is_distributed=True, is_sparse=True)
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64", lod_level=1) >>> label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64", lod_level=1)
ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1) >>> ones = paddle.static.data(name="ones", shape=[-1, 1], dtype="int64", lod_level=1)
show_clk = paddle.cast(paddle.concat([ones, label], axis=1), dtype='float32') >>> show_clk = paddle.cast(paddle.concat([ones, label], axis=1), dtype='float32')
show_clk.stop_gradient = True >>> show_clk.stop_gradient = True
cvms = paddle.incubate.layers.fused_seqpool_cvm(embs, 'sum', show_clk) >>> cvms = paddle.incubate.layers.fused_seqpool_cvm(embs, 'sum', show_clk)
""" """
...@@ -212,10 +213,10 @@ def multiclass_nms2( ...@@ -212,10 +213,10 @@ def multiclass_nms2(
In the NMS step, this operator greedily selects a subset of detection bounding In the NMS step, this operator greedily selects a subset of detection bounding
boxes that have high scores larger than score_threshold, if providing this boxes that have high scores larger than score_threshold, if providing this
threshold, then selects the largest nms_top_k confidences scores if nms_top_k threshold, then selects the largest nms_top_k confidences scores if nms_top_k
is larger than -1. Then this operator pruns away boxes that have high IOU is larger than -1. Then this operator prunes away boxes that have high IOU
(intersection over union) overlap with already selected boxes by adaptive (intersection over union) overlap with already selected boxes by adaptive
threshold NMS based on parameters of nms_threshold and nms_eta. threshold NMS based on parameters of nms_threshold and nms_eta.
Aftern NMS step, at most keep_top_k number of total bboxes are to be kept After NMS step, at most keep_top_k number of total bboxes are to be kept
per image if keep_top_k is larger than -1. per image if keep_top_k is larger than -1.
Args: Args:
...@@ -228,7 +229,7 @@ def multiclass_nms2( ...@@ -228,7 +229,7 @@ def multiclass_nms2(
[xmin, ymin, xmax, ymax], when box size equals to 4. [xmin, ymin, xmax, ymax], when box size equals to 4.
2. (LoDTensor) A 3-D Tensor with shape [M, C, 4] 2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
M is the number of bounding boxes, C is the M is the number of bounding boxes, C is the
class number class number.
scores (Tensor): Two types of scores are supported: scores (Tensor): Two types of scores are supported:
1. (Tensor) A 3-D Tensor with shape [N, C, M] 1. (Tensor) A 3-D Tensor with shape [N, C, M]
represents the predicted confidence predictions. represents the predicted confidence predictions.
...@@ -241,22 +242,22 @@ def multiclass_nms2( ...@@ -241,22 +242,22 @@ def multiclass_nms2(
M is the number of bbox, C is the class number. M is the number of bbox, C is the class number.
In this case, input BBoxes should be the second In this case, input BBoxes should be the second
case with shape [M, C, 4]. case with shape [M, C, 4].
background_label (int): The index of background label, the background
label will be ignored. If set to -1, then all
categories will be considered. Default: 0
score_threshold (float): Threshold to filter out bounding boxes with score_threshold (float): Threshold to filter out bounding boxes with
low confidence score. If not provided, low confidence score. If not provided,
consider all boxes. consider all boxes.
nms_top_k (int): Maximum number of detections to be kept according to nms_top_k (int): Maximum number of detections to be kept according to
the confidences after the filtering detections based the confidences after the filtering detections based
on score_threshold. on score_threshold.
nms_threshold (float): The threshold to be used in NMS. Default: 0.3
nms_eta (float): The threshold to be used in NMS. Default: 1.0
keep_top_k (int): Number of total bboxes to be kept per image after NMS keep_top_k (int): Number of total bboxes to be kept per image after NMS
step. -1 means keeping all bboxes after NMS step. step. -1 means keeping all bboxes after NMS step.
normalized (bool): Whether detections are normalized. Default: True nms_threshold (float, optional): The threshold to be used in NMS. Default: 0.3.
return_index(bool): Whether return selected index. Default: False normalized (bool, optional): Whether detections are normalized. Default: True.
name(str): Name of the multiclass nms op. Default: None. nms_eta (float, optional): The threshold to be used in NMS. Default: 1.0.
background_label (int, optional): The index of background label, the background
label will be ignored. If set to -1, then all
categories will be considered. Default: 0.
return_index(bool, optional): Whether return selected index. Default: False.
name(str, optional): Name of the multiclass nms op. Default: None.
Returns: Returns:
A tuple with two dimensions of the tensor: (Out, Index) if return_index is True, A tuple with two dimensions of the tensor: (Out, Index) if return_index is True,
...@@ -279,23 +280,21 @@ def multiclass_nms2( ...@@ -279,23 +280,21 @@ def multiclass_nms2(
Examples: Examples:
.. code-block:: python .. code-block:: python
>>> import paddle
import paddle.fluid as fluid >>> paddle.enable_static()
import paddle >>> boxes = paddle.static.data(name='bboxes', shape=[-1, 81, 4],
paddle.enable_static() ... dtype='float32', lod_level=1)
boxes = paddle.static.data(name='bboxes', shape=[-1, 81, 4], >>> scores = paddle.static.data(name='scores', shape=[-1, 81],
dtype='float32', lod_level=1) ... dtype='float32', lod_level=1)
scores = paddle.static.data(name='scores', shape=[-1, 81], >>> out, index = paddle.incubate.layers.multiclass_nms2(bboxes=boxes,
dtype='float32', lod_level=1) ... scores=scores,
out, index = paddle.incubate.layers.multiclass_nms2(bboxes=boxes, ... background_label=0,
scores=scores, ... score_threshold=0.5,
background_label=0, ... nms_top_k=400,
score_threshold=0.5, ... nms_threshold=0.3,
nms_top_k=400, ... keep_top_k=200,
nms_threshold=0.3, ... normalized=False,
keep_top_k=200, ... return_index=True)
normalized=False,
return_index=True)
""" """
helper = LayerHelper('multiclass_nms2', **locals()) helper = LayerHelper('multiclass_nms2', **locals())
...@@ -353,26 +352,27 @@ def search_pyramid_hash( ...@@ -353,26 +352,27 @@ def search_pyramid_hash(
pyramid_layer (int): The number of pyramid layers. It should be greater than 2. pyramid_layer (int): The number of pyramid layers. It should be greater than 2.
rand_len (int): The minimum length of pyramid hash cell. rand_len (int): The minimum length of pyramid hash cell.
drop_out_percent (float): The probability of dropping out the input token randomly. drop_out_percent (float): The probability of dropping out the input token randomly.
It should satisfy: [0., 1.] It should satisfy: [0., 1.].
is_training (bool): Whether in training or testing phrase. is_training (bool): Whether in training or testing phrase.
use_filter(bool): If set True, the white filter and black filter should be given by use_filter (bool): If set True, the white filter and black filter should be given by
:attr:`param_attr_wl` and :attr:`param_attr_bl` . :attr:`param_attr_wl` and :attr:`param_attr_bl` .
white_list_len(int): If set :math:`white_list_len>0` , white filter with shape [white_list_len, 1] white_list_len (int): If set :math:`white_list_len>0` , white filter with shape [white_list_len, 1]
should be provided by param_attr_wl. should be provided by param_attr_wl.
black_list_len(int): If set :math:`black_list_len>0` , black filter with shape [black_list_len, 1] black_list_len (int): If set :math:`black_list_len>0` , black filter with shape [black_list_len, 1]
should be provided by param_attr_bl. should be provided by param_attr_bl.
seed(int): The number of random seed. seed (int): The number of random seed.
lr(float): The learning rate of weight created by :attr:`param_attr` with shape [space_len+rand_len, 1] lr (float): The learning rate of weight created by :attr:`param_attr` with shape [space_len+rand_len, 1]
in this layer. in this layer.
param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the param_attr (ParamAttr, optional): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` .
param_attr_wl(ParamAttr): Specified parameters of white filter. param_attr_wl (ParamAttr, optional): Specified parameters of white filter. Default: None.
param_attr_bl(ParamAttr): Specified parameters of black filter. param_attr_bl (ParamAttr, optional): Specified parameters of black filter. Default: None.
distribute_update_vars(list[ParamAttr.name]): Decided which params should be updated in distribute training. distribute_update_vars(list[ParamAttr.name], optional): Decided which params should be updated in distribute training.
Used in Distribute Transpiler to create a trainer/server program. Used in Distribute Transpiler to create a trainer/server program. Default: None.
name(str, optional): The default value is None. Normally there is no need for user to set this property. 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` . For more information, please refer to :ref:`api_guide_Name` . Default: None.
dtype(str): The data type of output Tensor, float32. dtype (str, optional): The data type of output Tensor, float32. Default: float32.
Returns: Returns:
Tensor: LoDTensor of pyramid hash embedding. Tensor: LoDTensor of pyramid hash embedding.
""" """
...@@ -451,25 +451,25 @@ def shuffle_batch(x, seed=None): ...@@ -451,25 +451,25 @@ def shuffle_batch(x, seed=None):
:attr:`x` is a LoDTensor to be shuffled with shape :math:`[N_1, N_2, ..., N_k, D]` . Note that the last dim of input will not be shuffled. :attr:`x` is a LoDTensor to be shuffled with shape :math:`[N_1, N_2, ..., N_k, D]` . Note that the last dim of input will not be shuffled.
:math:`N_1 * N_2 * ... * N_k` numbers of elements with length :math:`D` will be shuffled randomly. :math:`N_1 * N_2 * ... * N_k` numbers of elements with length :math:`D` will be shuffled randomly.
For Example: Examples:
.. code-block:: text .. code-block:: text
Input: Input:
x.data = [[1, 2], [3, 4], [5, 6], [7, 8]] x.data = [[1, 2], [3, 4], [5, 6], [7, 8]]
x.dims = [4, 2] x.dims = [4, 2]
Attrs: Attrs:
seed = 2019 seed = 2019
Output: Output:
Out.data =[[7, 8], [1, 2], [3, 4], [5, 6]] Out.data =[[7, 8], [1, 2], [3, 4], [5, 6]]
Out.dims = [4, 2] Out.dims = [4, 2]
Args: Args:
x (Tensor): The input Tensor. The input Tensor is a N-D LoDTensor with type int, float32 or float64. x (Tensor): The input Tensor. The input Tensor is a N-D LoDTensor with type int, float32 or float64.
seed (None|int|Tensor): The start up seed. If set, seed will be set as the start up seed of shuffle engine. seed (None|int|Tensor, optional): The start up seed. If set, seed will be set as the start up seed of shuffle engine.
If not set(Default), start up seed of shuffle engine will be generated randomly. If not set(Default), start up seed of shuffle engine will be generated randomly. Default: None.
Returns: Returns:
Tensor: The shuffled LoDTensor with the same shape and lod as input. Tensor: The shuffled LoDTensor with the same shape and lod as input.
...@@ -478,11 +478,10 @@ def shuffle_batch(x, seed=None): ...@@ -478,11 +478,10 @@ def shuffle_batch(x, seed=None):
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid >>> import paddle
import paddle >>> paddle.enable_static()
paddle.enable_static() >>> x = paddle.static.data(name="x", shape=[-1, 4])
x = paddle.static.data(name="x", shape=[-1, 4]) >>> out = paddle.incubate.layers.shuffle_batch(x)
out = paddle.incubate.layers.shuffle_batch(x)
""" """
helper = LayerHelper('shuffle_batch', **locals()) helper = LayerHelper('shuffle_batch', **locals())
...@@ -526,7 +525,7 @@ def partial_concat(input, start_index=0, length=-1): ...@@ -526,7 +525,7 @@ def partial_concat(input, start_index=0, length=-1):
[9, 10, 11]] [9, 10, 11]]
output = partial_concat([x, y], start_index=0, length=2) output = partial_concat([x, y], start_index=0, length=2)
we get: We get:
output = [[0, 1, 6, 7], output = [[0, 1, 6, 7],
[3, 4, 9, 10]] [3, 4, 9, 10]]
...@@ -534,20 +533,22 @@ def partial_concat(input, start_index=0, length=-1): ...@@ -534,20 +533,22 @@ def partial_concat(input, start_index=0, length=-1):
Args: Args:
input(list): List of input Tensors with data type float32, float64, int32, input(list): List of input Tensors with data type float32, float64, int32,
int64. int64.
start_index(int32): The start index of each instance for partial concatenation. start_index(int32, optional): The start index of each instance for partial concatenation.
Default is 0. Default is 0.
length(int32): The length of each instance for partial concatenation. Default is -1. length(int32, optional): The length of each instance for partial concatenation. Default is -1.
Negative values for all elements after start_index. Negative values for all elements after start_index.
Returns: Returns:
Tensor: A Tensor with the same data type as input's. Tensor: A Tensor with the same data type as input's.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle >>> import paddle
x = paddle.randn(name="x", shape=[1,3], dtype="float32") >>> x = paddle.randn(name="x", shape=[1,3], dtype="float32")
y = paddle.randn(name="y", shape=[1,3], dtype="float32") >>> y = paddle.randn(name="y", shape=[1,3], dtype="float32")
concat = paddle.incubate.layers.partial_concat( >>> concat = paddle.incubate.layers.partial_concat(
[x, y], start_index=0, length=2) ... [x, y], start_index=0, length=2)
""" """
if not isinstance(input, list): if not isinstance(input, list):
warnings.warn( warnings.warn(
...@@ -584,6 +585,7 @@ def partial_sum(input, start_index=0, length=-1): ...@@ -584,6 +585,7 @@ def partial_sum(input, start_index=0, length=-1):
This Op exists in incubate layers, which means that it is not shown to the public. This Op exists in incubate layers, which means that it is not shown to the public.
Only 2-D Tensor or LodTensor input is supported. Slice and concat can only be Only 2-D Tensor or LodTensor input is supported. Slice and concat can only be
performed along the second dimension. performed along the second dimension.
.. code-block:: text .. code-block:: text
Given: Given:
...@@ -592,30 +594,29 @@ def partial_sum(input, start_index=0, length=-1): ...@@ -592,30 +594,29 @@ def partial_sum(input, start_index=0, length=-1):
y = [[6, 7 ,8], y = [[6, 7 ,8],
[9, 10, 11]] [9, 10, 11]]
output = partial_sum([x, y], start_index=0, length=2) output = partial_sum([x, y], start_index=0, length=2)
we get:
We get:
output = [[6, 8], output = [[6, 8],
[12, 14]] [12, 14]]
Args: Args:
input(list): List of input Tensors with data type float32, float64, int32, input (list): List of input Tensors with data type float32, float64, int32,
int64. int64.
start_index (int32, optional): The start index of each instance for partial sum. Default is 0.
length (int32, optional): The length of each instance for partial sum. Default is -1.
Returns: Returns:
Tensor: A Tensor with the same data type as input's. Tensor: A Tensor with the same data type as input's.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import numpy as np >>> import paddle
import paddle >>> paddle.enable_static()
paddle.enable_static()
>>> x = paddle.static.data(name="x", shape=[2, 3], dtype="float32")
x = paddle.static.data(name="x", shape=[2, 3], dtype="float32") >>> y = paddle.static.data(name="y", shape=[2, 3], dtype="float32")
y = paddle.static.data(name="y", shape=[2, 3], dtype="float32") >>> sum = paddle.incubate.layers.partial_sum([x,y], start_index=0, length=2)
sum = paddle.incubate.layers.partial_sum([x,y], start_index=0, length=2)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
xx = np.array([1,2,3,4,5,6]).reshape((2,3)).astype("float32")
yy = np.array([6,5,4,4,5,6]).reshape((2,3)).astype("float32")
out = exe.run(feed={"x":xx, "y":yy}, fetch_list=[sum])
""" """
for id, x in enumerate(input): for id, x in enumerate(input):
check_variable_and_dtype( check_variable_and_dtype(
...@@ -642,6 +643,7 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'): ...@@ -642,6 +643,7 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
**Tdm Child** **Tdm Child**
According to the input node_id on the given tree, return the corresponding child node_id and According to the input node_id on the given tree, return the corresponding child node_id and
whether child is a leaf node by leaf_mask value. whether child is a leaf node by leaf_mask value.
.. code-block:: text .. code-block:: text
Given: Given:
...@@ -650,25 +652,26 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'): ...@@ -650,25 +652,26 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
node_nums = 7 node_nums = 7
child_nums = 2 child_nums = 2
we get: We get:
child = [[5, 6], child = [[5, 6],
[0, 0]] [0, 0]]
leaf_mask = [[1, 1], leaf_mask = [[1, 1],
[0, 0]] [0, 0]]
Args: Args:
x(Tensor): Tensor contained the node_id information, dtype support int32/int64. x (Tensor): Tensor contained the node_id information, dtype support int32/int64.
node_nums(int): Number of total nodes. node_nums (int): Number of total nodes.
child_nums(int): Maximum number of child nodes per node. child_nums (int): Maximum number of child nodes per node.
param_attr(ParamAttr): To specify the tdm-tree-info parameter property. Default: None, which means the param_attr (ParamAttr, optional): To specify the tdm-tree-info parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in: ref: `api_fluid_ParamAttr`, should default weight parameter property is used. See usage for details in: ref: `api_fluid_ParamAttr`, should
has shape(node_nums, 3 + child_nums), dtype support int32/int64. has shape (node_nums, 3 + child_nums), dtype support int32/int64.
The dimension[1] of tdm-tree-info contains the following: The dimension[1] of tdm-tree-info contains the following:
1. Item_id(int, shape(1)), if node is a leaf node, give its item_id corresponding to node_id, else give 0. 1. Item_id (int, shape(1)), if node is a leaf node, give its item_id corresponding to node_id, else give 0.
2. Layer_id(int, shape(1)), indicates which layer the node is on. 2. Layer_id (int, shape(1)), indicates which layer the node is on.
3. Parent_id(int, shape(1)), node's parent node. 3. Parent_id (int, shape(1)), node's parent node.
4. Child_id(int, shape(child_nums)), all child node's node_id of this node should be given. 4. Child_id (int, shape(child_nums)), all child node's node_id of this node should be given.
If the number of child nodes is insufficient, padding 0 until child nums equal to child_nums If the number of child nodes is insufficient, padding 0 until child nums equal to child_nums.
dtype(str): The data type of output child and leaf_mask, support int32/int64. dtype (str, optional): The data type of output child and leaf_mask, support int32/int64. Default: int32.
Returns: Returns:
tuple: A tuple including input node's child(Tensor) and leaf_mask(Tensor). tuple: A tuple including input node's child(Tensor) and leaf_mask(Tensor).
...@@ -676,27 +679,23 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'): ...@@ -676,27 +679,23 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle
import paddle.fluid as fluid >>> import paddle
import numpy as np >>> import numpy as np
paddle.enable_static() >>> paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1], dtype="int32", lod_level=1)
tree_info = [[0,0,0,1,2], >>> x = paddle.static.data(name="x", shape=[None, 1], dtype="int32", lod_level=1)
[0,1,0,3,4],[0,1,0,5,6], >>> tree_info = [[0,0,0,1,2],
[0,2,1,0,0],[1,2,1,0,0],[2,2,2,0,0],[3,2,2,0,0]] ... [0,1,0,3,4],[0,1,0,5,6],
tree_info_np = np.array(tree_info) ... [0,2,1,0,0],[1,2,1,0,0],[2,2,2,0,0],[3,2,2,0,0]]
tree_info_np = np.reshape(tree_info_np, (7,5)) >>> tree_info_np = np.array(tree_info)
node_nums = 7 >>> tree_info_np = np.reshape(tree_info_np, (7,5))
child_nums = 2 >>> node_nums = 7
child, leaf_mask = paddle.incubate.layers.tdm_child(x, node_nums, child_nums, >>> child_nums = 2
param_attr=fluid.ParamAttr( >>> child, leaf_mask = paddle.incubate.layers.tdm_child(x, node_nums, child_nums,
initializer=paddle.nn.initializer.Assign( ... param_attr=paddle.ParamAttr(
tree_info_np))) ... initializer=paddle.nn.initializer.Assign(tree_info_np)))
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
xx = np.array([[2],[3]]).reshape((2,1)).astype("int32")
child_res, leaf_mask_res = exe.run(feed={"x":xx}, fetch_list=[child, leaf_mask])
""" """
helper = LayerHelper("tdm_child", **locals()) helper = LayerHelper("tdm_child", **locals())
check_dtype( check_dtype(
...@@ -740,6 +739,7 @@ def tdm_sampler( ...@@ -740,6 +739,7 @@ def tdm_sampler(
""" """
**Tdm Sampler** **Tdm Sampler**
According to the input positive samples at leaf node(x), do negative sampling layer by layer on the given tree. According to the input positive samples at leaf node(x), do negative sampling layer by layer on the given tree.
.. code-block:: text .. code-block:: text
Given: Given:
...@@ -753,7 +753,7 @@ def tdm_sampler( ...@@ -753,7 +753,7 @@ def tdm_sampler(
leaf_node_num = 4 leaf_node_num = 4
output_list = False output_list = False
we get: We get:
out = [[1, 3], [1, 4], [2, 5], [2, 6]] out = [[1, 3], [1, 4], [2, 5], [2, 6]]
labels = [[1, 1], [1, 1], [1, 1], [1, 1]] labels = [[1, 1], [1, 1], [1, 1], [1, 1]]
mask = [[1, 1], [1, 1], [1, 1], [1, 1]] mask = [[1, 1], [1, 1], [1, 1], [1, 1]]
...@@ -763,21 +763,21 @@ def tdm_sampler( ...@@ -763,21 +763,21 @@ def tdm_sampler(
neg_samples_num_list (list(int)): Number of negative samples per layer. neg_samples_num_list (list(int)): Number of negative samples per layer.
layer_node_num_list (list(int)): Number of nodes per layer, must has same shape with neg_samples_num_list. layer_node_num_list (list(int)): Number of nodes per layer, must has same shape with neg_samples_num_list.
leaf_node_num (int): Number of leaf nodes. leaf_node_num (int): Number of leaf nodes.
tree_travel_attr (ParamAttr): To specify the tdm-travel parameter property. Default: None, which means the tree_travel_attr (ParamAttr, optional): To specify the tdm-travel parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr`, should default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr`, should
has shape (leaf_node_num, len(layer_node_num_list)), dtype support int32/int64. has shape (leaf_node_num, len(layer_node_num_list)), dtype support int32/int64.
tree_layer_attr (ParamAttr): To specify the tdm-layer parameter property. Default: None, which means the tree_layer_attr (ParamAttr, optional): To specify the tdm-layer parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr`, should default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr`, should
has shape (node_num, 1), dtype support int32/int64. has shape (node_num, 1), dtype support int32/int64.
output_positive (bool): Whether to output positive samples (includ label and mask )at the same time. output_positive (bool, optional): Whether to output positive samples (include label and mask )at the same time. Default: True.
output_list (bool): Whether to divide the output into layers and organize it into list format. output_list (bool, optional): Whether to divide the output into layers and organize it into list format. Default: True.
seed (int): The number of random seed. seed (int, optional): The number of random seed. Default: 0.
tree_dtype(np.dtype|core.VarDesc.VarType|str): The dtype of tdm-travel and tdm-layer, support int32/int64 tree_dtype (np.dtype|core.VarDesc.VarType|str, optional): The dtype of tdm-travel and tdm-layer, support int32/int64. Default: int32.
dtype(np.dtype|core.VarDesc.VarType|str): The dtype of output(sampling results, labels and masks) dtype (np.dtype|core.VarDesc.VarType|str, optional): The dtype of output(sampling results, labels and masks). Default: int32.
Returns: Returns:
tuple: A tuple including sampling results, corresponding labels and masks. if output_positive = True, sampling tuple: A tuple including sampling results, corresponding labels and masks. if output_positive = True, sampling
result will include both positive and negative samples. If sampling reseult is a positive sample, the label is 1, result will include both positive and negative samples. If sampling result is a positive sample, the label is 1,
and if it is a negative sample, it is 0. If the tree is unbalanced, in order to ensure the consistency of the and if it is a negative sample, it is 0. If the tree is unbalanced, in order to ensure the consistency of the
sampling result shape, the padding sample's mask = 0, the real sample's mask value = 1. sampling result shape, the padding sample's mask = 0, the real sample's mask value = 1.
If output_list = True, the result will organize into list format specified by layer information. If output_list = True, the result will organize into list format specified by layer information.
...@@ -785,43 +785,37 @@ def tdm_sampler( ...@@ -785,43 +785,37 @@ def tdm_sampler(
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle
import paddle.fluid as fluid >>> import paddle
import numpy as np >>> import numpy as np
paddle.enable_static() >>> paddle.enable_static()
x = paddle.static.data(name="x", shape=[None, 1], dtype="int32", lod_level=1)
travel_list = [[1, 3], [1, 4], [2, 5], [2, 6]] # leaf node's travel path, shape(leaf_node_num, layer_num) >>> x = paddle.static.data(name="x", shape=[None, 1], dtype="int32", lod_level=1)
layer_list_flat = [[1], [2], [3], [4], [5], [6]] # shape(node_nums, 1) >>> travel_list = [[1, 3], [1, 4], [2, 5], [2, 6]] # leaf node's travel path, shape(leaf_node_num, layer_num)
>>> layer_list_flat = [[1], [2], [3], [4], [5], [6]] # shape(node_nums, 1)
neg_samples_num_list = [0, 0] # negative sample nums = 0
layer_node_num_list = [2, 4] #two layer (exclude root node) >>> neg_samples_num_list = [0, 0] # negative sample nums = 0
leaf_node_num = 4 >>> layer_node_num_list = [2, 4] #two layer (exclude root node)
>>> leaf_node_num = 4
travel_array = np.array(travel_list)
layer_array = np.array(layer_list_flat) >>> travel_array = np.array(travel_list)
>>> layer_array = np.array(layer_list_flat)
sample, label, mask = paddle.incubate.layers.tdm_sampler(
x, >>> sample, label, mask = paddle.incubate.layers.tdm_sampler(
neg_samples_num_list, ... x,
layer_node_num_list, ... neg_samples_num_list,
leaf_node_num, ... layer_node_num_list,
tree_travel_attr=fluid.ParamAttr( ... leaf_node_num,
initializer=paddle.nn.initializer.Assign( ... tree_travel_attr=paddle.ParamAttr(
travel_array)), ... initializer=paddle.nn.initializer.Assign(
tree_layer_attr=fluid.ParamAttr( ... travel_array)),
initializer=paddle.nn.initializer.Assign( ... tree_layer_attr=paddle.ParamAttr(
layer_array)), ... initializer=paddle.nn.initializer.Assign(
output_positive=True, ... layer_array)),
output_list=True, ... output_positive=True,
seed=0, ... output_list=True,
tree_dtype='int32') ... seed=0,
... tree_dtype='int32')
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
xx = np.array([[0],[1]]).reshape((2,1)).astype("int32")
exe.run(feed={"x":xx})
""" """
helper = LayerHelper("tdm_sampler", **locals()) helper = LayerHelper("tdm_sampler", **locals())
...@@ -968,30 +962,33 @@ def rank_attention( ...@@ -968,30 +962,33 @@ def rank_attention(
rank_param gives the organization of data. Notice: It currently supports rank_param gives the organization of data. Notice: It currently supports
GPU device. GPU device.
This Op exists in incubate layers, which means that it is not shown to the public. This Op exists in incubate layers, which means that it is not shown to the public.
Args: Args:
input: Tensor with data type float32, float64. input (Tensor): Tensor with data type float32, float64.
rank_offset: Tensor with data type int32. rank_offset (Tensor): Tensor with data type int32.
rank_para_shape: The shape of rank_param. rank_para_shape (list[int]): The shape of rank_param.
rank_param_attr: Attribute initializer of rank_param. rank_param_attr (ParamAttr): Attribute initializer of rank_param.
max_rank: The max rank of input's ranks. max_rank (int, optional): The max rank of input's ranks. Default is 3.
max_size (int, optional): The max size of input's ranks. Default is 0.
Returns: Returns:
Tensor: A Tensor with the same data type as input's. Tensor: A Tensor with the same data type as input's.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle >>> import paddle
paddle.enable_static() >>> paddle.enable_static()
input = paddle.static.data(name="input", shape=[None, 2], dtype="float32") >>> input = paddle.static.data(name="input", shape=[None, 2], dtype="float32")
rank_offset = paddle.static.data(name="rank_offset", shape=[None, 7], dtype="int32") >>> rank_offset = paddle.static.data(name="rank_offset", shape=[None, 7], dtype="int32")
out = paddle.incubate.layers.rank_attention(input=input, >>> out = paddle.incubate.layers.rank_attention(input=input,
rank_offset=rank_offset, ... rank_offset=rank_offset,
rank_param_shape=[18,3], ... rank_param_shape=[18,3],
rank_param_attr= ... rank_param_attr=
paddle.ParamAttr(learning_rate=1.0, ... paddle.ParamAttr(learning_rate=1.0,
name="ubm_rank_param.w_0"), ... name="ubm_rank_param.w_0"),
max_rank=3, ... max_rank=3,
max_size=0) ... max_size=0)
""" """
helper = LayerHelper('rank_attention', **locals()) helper = LayerHelper('rank_attention', **locals())
dtype = helper.input_dtype(input_param_name='input') dtype = helper.input_dtype(input_param_name='input')
...@@ -1027,34 +1024,35 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None): ...@@ -1027,34 +1024,35 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None):
except that the bias and relu activation layers are added. except that the bias and relu activation layers are added.
Notice: It currently supports GPU device. Notice: It currently supports GPU device.
This Op exists in incubate layers, which means that it is not shown to the public. This Op exists in incubate layers, which means that it is not shown to the public.
Args: Args:
input: Tensor with data type float32, float64. input (Tensor): Tensor with data type float32, float64.
param_size: The size of w. param_size (list[int]): The size of w.
param_attr: Attribute initializer of w. param_attr (ParamAttr): Attribute initializer of w.
bias_size: The size of bias. bias_size (list[int]): The size of bias.
bias_attr: Attribute initializer of bias. bias_attr (ParamAttr): Attribute initializer of bias.
act: Activation to be applied to the output of this layer. act (str, optional): Activation to be applied to the output of this layer. Default is None.
Returns: Returns:
Tensor: A Tensor with the same data type as input's. Tensor: A Tensor with the same data type as input's.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
import paddle >>> import paddle
>>> paddle.enable_static()
paddle.enable_static()
>>> input = paddle.static.data(name="input", shape=[16, 2, 3], dtype="float32")
input = paddle.static.data(name="input", shape=[16, 2, 3], dtype="float32") >>> out = paddle.incubate.layers.batch_fc(input=input,
out = paddle.incubate.layers.batch_fc(input=input, ... param_size=[16, 3, 10],
param_size=[16, 3, 10], ... param_attr=
param_attr= ... paddle.ParamAttr(learning_rate=1.0,
paddle.ParamAttr(learning_rate=1.0, ... name="w_0"),
name="w_0"), ... bias_size=[16, 10],
bias_size=[16, 10], ... bias_attr=
bias_attr= ... paddle.ParamAttr(learning_rate=1.0,
paddle.ParamAttr(learning_rate=1.0, ... name="b_0"),
name="b_0"), ... act="relu")
act="relu")
""" """
helper = LayerHelper("batch_fc", **locals()) helper = LayerHelper("batch_fc", **locals())
...@@ -1089,23 +1087,26 @@ def _pull_box_extended_sparse(input, size, extend_size=64, dtype='float32'): ...@@ -1089,23 +1087,26 @@ def _pull_box_extended_sparse(input, size, extend_size=64, dtype='float32'):
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
BoxPS lookup table. The result of this lookup is the embedding of each ID in the BoxPS lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`. :attr:`input`.
Args: Args:
input(Tensor): Input is a Tensor<int64>, which input (Tensor): Input is a Tensor<int64>, which contains the IDs information.
contains the IDs information. size (int): The embedding size parameter, which indicates the size of
size(int): The embedding size parameter, which indicates the size of
each embedding vector respectively. each embedding vector respectively.
extend_size(int): The embedding size parameter in extended dim, extend_size (int, optional): The embedding size parameter in extended dim,
which indicates the size of each embedding vector respectively. which indicates the size of each embedding vector respectively. Default is 64.
dtype(str): The dtype refers to the data type of output tensor. Only supports dtype (str, optional): The dtype refers to the data type of output tensor. Only supports float32 now. Default is float32.
float32 now.
Returns: Returns:
Tensor: The tensor storing the embeddings of the \ Tensor: The tensor storing the embeddings of the supplied inputs.
supplied inputs.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid
data = paddle.static.data(name='sequence', shape=[-1, 1], dtype='int64', lod_level=1) >>> import paddle
emb, emb_ex = paddle.incubate.layers._pull_box_extended_sparse(input=data, size=8, extend_size=128) >>> paddle.enable_static()
>>> data = paddle.static.data(name='sequence', shape=[-1, 1], dtype='int64', lod_level=1)
>>> emb, emb_ex = paddle.incubate.layers._pull_box_extended_sparse(input=data, size=8, extend_size=128)
""" """
helper = LayerHelper('pull_box_extended_sparse', **locals()) helper = LayerHelper('pull_box_extended_sparse', **locals())
helper.input_dtype() helper.input_dtype()
...@@ -1139,16 +1140,16 @@ def bilateral_slice(x, guide, grid, has_offset, name=None): ...@@ -1139,16 +1140,16 @@ def bilateral_slice(x, guide, grid, has_offset, name=None):
For more information of bilateral slicing, please refer to Deep Bilateral Learning for Real-Time Image Enhancement <https://groups.csail.mit.edu/graphics/hdrnet/data/hdrnet.pdf>_ For more information of bilateral slicing, please refer to Deep Bilateral Learning for Real-Time Image Enhancement <https://groups.csail.mit.edu/graphics/hdrnet/data/hdrnet.pdf>_
Args: Args:
x(Tensor): The input tensor, which is a 4-D tensor with shape x (Tensor): The input tensor, which is a 4-D tensor with shape
[N, C, H, W], N is the batch size, C is the channel [N, C, H, W], N is the batch size, C is the channel
number, H and W is the feature height and width. number, H and W is the feature height and width.
The data type is float32 and float64. The data type is float32 and float64.
guide(Tensor): Input grid tensor of shape [N, H, W]. The guide (Tensor): Input grid tensor of shape [N, H, W]. The
data type is float32 and float64. data type is float32 and float64.
grid(Tensor): Input grid tensor of shape [N, C, D, H, W]. The grid (Tensor): Input grid tensor of shape [N, C, D, H, W]. The
data type is float32 and float64. data type is float32 and float64.
has_offset(bool): Whether to slice with affine offset. has_offset (bool): Whether to slice with affine offset.
name(str, optional): For detailed information, please refer name (str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
...@@ -1159,19 +1160,18 @@ def bilateral_slice(x, guide, grid, has_offset, name=None): ...@@ -1159,19 +1160,18 @@ def bilateral_slice(x, guide, grid, has_offset, name=None):
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid >>> import paddle
import paddle >>> paddle.enable_static()
paddle.enable_static()
x = paddle.randn(name='x', shape=[1, 3, 101, 60], dtype='float32') >>> x = paddle.randn(name='x', shape=[1, 3, 101, 60], dtype='float32')
guide = paddle.randn(name='guide', shape=[1, 101, 60], dtype='float32') >>> guide = paddle.randn(name='guide', shape=[1, 101, 60], dtype='float32')
grid = paddle.randn(name='grid', shape=[1, 12, 8, 10, 6], dtype='float32') >>> grid = paddle.randn(name='grid', shape=[1, 12, 8, 10, 6], dtype='float32')
# without offset >>> # without offset
output = paddle.incubate.layers.bilateral_slice(x, guide, grid, has_offset=False) >>> output = paddle.incubate.layers.bilateral_slice(x, guide, grid, has_offset=False)
# has offset >>> # has offset
output = paddle.incubate.layers.bilateral_slice(x, guide, grid, has_offset=True) >>> output = paddle.incubate.layers.bilateral_slice(x, guide, grid, has_offset=True)
""" """
if paddle.in_dynamic_mode(): if paddle.in_dynamic_mode():
...@@ -1215,13 +1215,13 @@ def correlation( ...@@ -1215,13 +1215,13 @@ def correlation(
<https://arxiv.org/pdf/1709.02371.pdf>_ <https://arxiv.org/pdf/1709.02371.pdf>_
Args: Args:
x(Tensor): The input x is 4-D Tensor with shape [N, C, H, W]. The data type is float32 and float64. x (Tensor): The input x is 4-D Tensor with shape [N, C, H, W]. The data type is float32 and float64.
y(Tensor): The input y is 4-D Tensor with shape [N, C, H, W]. The data type is float32 and float64. y (Tensor): The input y is 4-D Tensor with shape [N, C, H, W]. The data type is float32 and float64.
pad_size(int): Pad size. The data type is int. pad_size (int): Pad size. The data type is int.
max_displacement(int): Max displacement. The data type is int. max_displacement (int): Max displacement. The data type is int.
stride1(int): stride size of x. The data type is int. stride1 (int): stride size of x. The data type is int.
stride2(int): stride size of y. The data type is int. stride2 (int): stride size of y. The data type is int.
corr_type_multiply(int, optional): The type of multiply. The data type is int. Default: 1. corr_type_multiply (int, optional): The type of multiply. The data type is int. Default: 1.
Returns: Returns:
Tensor: The data type is same as input tensor. Tensor: The data type is same as input tensor.
...@@ -1230,25 +1230,24 @@ def correlation( ...@@ -1230,25 +1230,24 @@ def correlation(
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid >>> import paddle
import paddle >>> paddle.enable_static()
paddle.enable_static() >>> x1 = paddle.static.data(name='x1',
x1 = paddle.static.data(name='x1', ... shape=[2, 3, 4, 5],
shape=[2,3,4,5], ... dtype="float32")
dtype="float32") >>> x2 = paddle.static.data(name='x2',
x2 = paddle.static.data(name='x2', ... shape=[2, 3, 4, 5],
shape=[2,3,4,5], ... dtype="float32")
dtype="float32")
>>> out = paddle.incubate.layers.correlation(
out = paddle.incubate.layers.correlation( ... x1,
x1, ... x2,
x2, ... pad_size=4,
pad_size=4, ... kernel_size=1,
kernel_size=1, ... max_displacement=4,
max_displacement=4, ... stride1=1,
stride1=1, ... stride2=1)
stride2=1)
""" """
...@@ -1305,105 +1304,97 @@ def fused_bn_add_act( ...@@ -1305,105 +1304,97 @@ def fused_bn_add_act(
`[batch, in_height, in_width, in_channels]`. `[batch, in_height, in_width, in_channels]`.
Args: Args:
x(Tensor): The rank of input tensor can be 2, 3, 4, 5. The data type x (Tensor): The rank of input tensor can be 2, 3, 4, 5. The data type
is float16. is float16.
y(Tensor): The rank of input tensor can be 2, 3, 4, 5. The data type y (Tensor): The rank of input tensor can be 2, 3, 4, 5. The data type
is float16. is float16.
momentum(float|Tensor, optional): The value used for the moving_mean and momentum (float|Tensor, optional): The value used for the moving_mean and
moving_var computation. This should be a float number or a tensor with moving_var computation. This should be a float number or a tensor with
shape [1] and data type as float32. The updated formula is: shape [1] and data type as float32. The updated formula is:
:math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)` :math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)`
:math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)` :math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)`
Default is 0.9. Default is 0.9.
epsilon(float, optional): A value added to the denominator for epsilon (float, optional): A value added to the denominator for
numerical stability. Default is 1e-5. numerical stability. Default is 1e-05.
param_attr(ParamAttr, optional): The parameter attribute for Parameter `scale` param_attr (ParamAttr, optional): The parameter attribute for Parameter `scale`
of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
will create ParamAttr as param_attr, the name of scale can be set in ParamAttr. will create ParamAttr as param_attr, the name of scale can be set in ParamAttr.
If the Initializer of the param_attr is not set, the parameter is initialized If the Initializer of the param_attr is not set, the parameter is initialized
with Xavier. Default: None. with Xavier. Default: None.
bias_attr(ParamAttr, optional): The parameter attribute for the bias of batch_norm. bias_attr (ParamAttr, optional): The parameter attribute for the bias of batch_norm.
If it is set to None or one attribute of ParamAttr, batch_norm If it is set to None or one attribute of ParamAttr, batch_norm
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero. If the Initializer of the bias_attr is not set, the bias is initialized zero.
Default: None. Default: None.
moving_mean_name(str, optional): The name of moving_mean which store the global Mean. If it moving_mean_name (str, optional): The name of moving_mean which store the global Mean. If it
is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm
will save global mean with the string. will save global mean with the string. Default: None.
moving_variance_name(str, optional): The name of the moving_variance which store the global Variance. moving_variance_name (str, optional): The name of the moving_variance which store the global Variance.
If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm
will save global variance with the string. will save global variance with the string. Default: None.
act(string, optional): Activation type, linear|relu|prelu|... act (string, optional): Activation type, linear|relu|prelu|... Default: None.
name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. name (str, optional): For detailed information, please refer to :ref:`api_guide_Name`.
Usually name is no need to set and None by default. Usually name is no need to set and None by default. Default: None.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> # doctest: +REQUIRES(env:GPU)
import paddle.fluid as fluid >>> import paddle
>>> paddle.enable_static()
paddle.enable_static()
# required: gpu >>> def build_program(main_program, startup_program):
def build_program(main_program, startup_program): ... with paddle.static.program_guard(main_program, startup_program):
with fluid.program_guard(main_program, startup_program): ... x = paddle.static.data(name='x', shape=[-1, 1, 28, 28], dtype='float32')
x = paddle.static.data(name='x', shape=[-1, 1, 28, 28], dtype='float32') ... y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64') ... conv1_1 = paddle.static.nn.conv2d(
conv1_1 = paddle.static.nn.conv2d( ... input=x,
input=x, ... filter_size=3,
filter_size=3, ... num_filters=32,
num_filters=32, ... stride=1,
stride=1, ... padding=1,
padding=1, ... act=None,
act=None, ... bias_attr=False,
bias_attr=False, ... data_format='NHWC')
data_format='NHWC') ... conv1_2 = paddle.static.nn.conv2d(
conv1_2 = paddle.static.nn.conv2d( ... input=x,
input=x, ... filter_size=3,
filter_size=3, ... num_filters=32,
num_filters=32, ... stride=1,
stride=1, ... padding=1,
padding=1, ... act=None,
act=None, ... bias_attr=False,
bias_attr=False, ... data_format='NHWC')
data_format='NHWC') ... bn = paddle.static.nn.batch_norm(
bn = paddle.static.nn.batch_norm( ... input=conv1_1,
input=conv1_1, ... act=None,
act=None, ... data_layout='NHWC')
data_layout='NHWC') ... fused_bn_add_act = paddle.incubate.layers.fused_bn_add_act(conv1_2, bn)
fused_bn_add_act = paddle.incubate.layers.fused_bn_add_act(conv1_2, bn) ... prediction = paddle.static.nn.fc(x=fused_bn_add_act, size=10, activation='softmax')
prediction = paddle.static.nn.fc(x=fused_bn_add_act, size=10, activation='softmax') ... loss = paddle.nn.functional.cross_entropy(
loss = paddle.nn.functional.cross_entropy( ... input=prediction, label=y,
input=prediction, label=y, ... reduction='none', use_softmax=False
reduction='none', use_softmax=False ... )
) ... loss = paddle.mean(loss)
loss = paddle.mean(loss) ... sgd = paddle.optimizer.SGD(learning_rate=0.001)
sgd = fluid.optimizer.SGD(learning_rate=0.001) ... sgd = paddle.static.amp.decorate(
sgd = paddle.static.amp.decorate( ... sgd, use_dynamic_loss_scaling=True, init_loss_scaling=128.0)
sgd, use_dynamic_loss_scaling=True, init_loss_scaling=128.0) ... sgd.minimize(loss)
sgd.minimize(loss) ...
... return x, y, loss
return x, y, loss
>>> iters = 5
iters = 5 >>> batch_size = 16
batch_size = 16 >>> support_gpu = paddle.is_compiled_with_cuda()
support_gpu = fluid.is_compiled_with_cuda() >>> if support_gpu:
if support_gpu: ... main_program = paddle.static.Program()
main_program = fluid.Program() ... startup_program = paddle.static.Program()
startup_program = fluid.Program() ... place = paddle.CUDAPlace(0)
place = fluid.CUDAPlace(0) ... x, y, loss = build_program(main_program, startup_program)
x, y, loss = build_program(main_program, startup_program) ...
... feeder = paddle.DataFeeder(feed_list=[x, y], place=place)
feeder = fluid.DataFeeder(feed_list=[x, y], place=place) ... train_reader = paddle.batch(
train_reader = paddle.batch( ... paddle.dataset.mnist.train(), batch_size=batch_size)
paddle.dataset.mnist.train(), batch_size=batch_size)
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup_program)
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(main_program, feed=feeder.feed(data), fetch_list=[loss])
""" """
helper = LayerHelper('fused_bn_add_act', **locals()) helper = LayerHelper('fused_bn_add_act', **locals())
...@@ -1550,27 +1541,29 @@ def _pull_gpups_sparse( ...@@ -1550,27 +1541,29 @@ def _pull_gpups_sparse(
:attr:`input`. :attr:`input`.
Args: Args:
input(Tensor): Input is a Tensor<int64>, which input (Tensor): Input is a Tensor<int64>, which contains the IDs information.
contains the IDs information. size (int|list of int): The embedding size parameter of each input, which indicates the size of
size(int|list of int): The embedding size parameter of each input, which indicates the size of
each embedding vector respectively. each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports dtype (str, optional): The dtype refers to the data type of output tensor. Only supportsfloat32 now. Default is float32.
float32 now. is_distributed (bool, optional): Whether to use distributed mode. Default is False.
is_sparse (bool, optional): Whether to use sparse mode. Default is False.
Returns: Returns:
Tensor: The tensor storing the embeddings of the \ Tensor: The tensor storing the embeddings of the supplied inputs, whose size are indicated by size respectively.
supplied inputs, whose size are indicated by size respectively.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.incubate as incubate >>> import paddle.incubate as incubate
slots = [] >>> import paddle
data_1 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1) >>> paddle.enable_static()
slots.append(data_1)
data_2 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1) >>> slots = []
slots.append(data_2) >>> data_1 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
embs = incubate.layers.pull_gpups_sparse(input=slots, size=[11, 35]) >>> slots.append(data_1)
>>> data_2 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
>>> slots.append(data_2)
>>> embs = incubate.layers.pull_gpups_sparse(input=slots, size=[11, 35])
""" """
helper = LayerHelper('pull_gpups_sparse', **locals()) helper = LayerHelper('pull_gpups_sparse', **locals())
if dtype != 'float32': if dtype != 'float32':
...@@ -1613,23 +1606,26 @@ def _pull_box_sparse( ...@@ -1613,23 +1606,26 @@ def _pull_box_sparse(
:attr:`input`. :attr:`input`.
Args: Args:
input(Tensor): Input is a Tensor<int64>, which input (Tensor): Input is a Tensor<int64>, which contains the IDs information.
contains the IDs information. size (int): The embedding size parameter, which indicates the size of
size(int): The embedding size parameter, which indicates the size of
each embedding vector respectively. each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports dtype (str, optional): The dtype refers to the data type of output tensor. Only supports float32 now. Default is float32.
float32 now. is_distributed (bool, optional): Whether to use distributed mode. Default is False.
is_sparse (bool, optional): Whether to use sparse mode. Default is False.
Returns: Returns:
Tensor: The tensor storing the embeddings of the \ Tensor: The tensor storing the embeddings of the supplied inputs.
supplied inputs.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.incubate as incubate >>> import paddle.incubate as incubate
data = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1) >>> import paddle
emb = incubate.layers.pull_box_sparse(input=data, size=[11]) >>> paddle.enable_static()
>>> x = paddle.static.data(name='x', shape=[-1, 1], dtype='int64', lod_level=1)
>>> y = paddle.static.data(name='y', shape=[-1, 1], dtype='int64', lod_level=1)
>>> emb_x, emb_y = incubate.layers._pull_box_sparse([x, y], size=1)
""" """
helper = LayerHelper('pull_box_sparse', **locals()) helper = LayerHelper('pull_box_sparse', **locals())
if dtype != 'float32': if dtype != 'float32':
......
python/paddle/incubate/nn/functional/fused_dropout_add.py
浏览文件 @ 4dbe441c
...@@ -51,15 +51,27 @@ def fused_dropout_add( ...@@ -51,15 +51,27 @@ def fused_dropout_add(
Examples: Examples:
.. code-block:: python .. code-block:: python
# required: gpu >>> # doctest: +REQUIRES(env:GPU)
import paddle >>> import paddle
from paddle.incubate.nn.functional import fused_dropout_add >>> from paddle.incubate.nn.functional import fused_dropout_add
x = paddle.randn([4, 10], dtype='float16') >>> paddle.set_device('gpu')
y = paddle.randn([4, 10], dtype='float16') >>> paddle.seed(2023)
out = fused_dropout_add(x, y, p=0.5) >>> x = paddle.randn([4, 10], dtype="float32")
>>> y = paddle.randn([4, 10], dtype="float32")
>>> out = fused_dropout_add(x, y, p=0.5)
>>> print(out)
Tensor(shape=[4, 10], dtype=float32, place=Place(gpu:0), stop_gradient=True,
[[-0.49133155, 0.53819323, -2.58393312, 0.06336236, -1.09908366,
0.22085167, 2.19751787, 0.05034769, 0.53417486, 0.84864247],
[ 0.78248203, -1.59652555, -0.14399840, -0.77985179, -0.17006736,
-0.30991879, -0.36593807, -0.51025450, 1.46401680, 0.61627960],
[ 4.50472546, -0.48472026, 0.60729283, 0.33509624, -0.25593102,
-1.45173049, 1.06727099, 0.00440830, -0.77340341, 0.67393088],
[ 1.29453969, 0.07568165, 0.71947742, -0.71768606, -2.57172823,
1.89179027, 3.26482797, 1.10493207, -1.04569530, -1.04862499]])
""" """
if isinstance(p, (int, float)): if isinstance(p, (int, float)):
# fast return for p == 0 # fast return for p == 0
......
python/paddle/incubate/nn/functional/fused_ec_moe.py
浏览文件 @ 4dbe441c
...@@ -37,25 +37,20 @@ def fused_ec_moe( ...@@ -37,25 +37,20 @@ def fused_ec_moe(
Examples: Examples:
.. code-block:: python .. code-block:: python
# required: gpu >>> # doctest: +REQUIRES(env:GPU)
import paddle >>> import paddle
from paddle.incubate.nn.functional import fused_ec_moe >>> from paddle.incubate.nn.functional import fused_ec_moe
batch = 10 >>> paddle.set_device('gpu')
seq_len = 128 >>> x = paddle.randn([10, 128, 1024])
d_model = 1024 >>> gate = paddle.randn([10, 128, 8])
d_feed_forward = d_model * 4 >>> bmm0_weight = paddle.randn([8, 1024, 4096])
num_expert = 8 >>> bmm0_bias = paddle.randn([8, 1024, 4096])
>>> bmm1_weight = paddle.randn([8, 1024, 4096])
x = paddle.randn([batch, seq_len, d_model]) >>> bmm1_bias = paddle.randn([8, 1024, 4096])
gate = paddle.randn([batch, seq_len, num_expert]) >>> out = fused_ec_moe(x, gate, bmm0_weight, bmm0_bias, bmm1_weight, bmm1_bias, act_type="gelu")
bmm0_weight = paddle.randn([num_expert, d_model, d_feed_forward]) >>> print(out.shape)
bmm0_bias = paddle.randn([num_expert, d_model, d_feed_forward]) [10, 128, 1024]
bmm1_weight = paddle.randn([num_expert, d_model, d_feed_forward])
bmm1_bias = paddle.randn([num_expert, d_model, d_feed_forward])
out = fused_ec_moe(x, gate, bmm0_weight, bmm0_bias, bmm1_weight, bmm1_bias, act_type="gelu")
print(out.shape) # [batch, seq_len, num_expert]
""" """
helper = LayerHelper('fused_moe', **locals()) helper = LayerHelper('fused_moe', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype) out = helper.create_variable_for_type_inference(dtype=x.dtype)
......
python/paddle/incubate/nn/functional/fused_gate_attention.py
浏览文件 @ 4dbe441c
...@@ -39,7 +39,7 @@ def fused_gate_attention( ...@@ -39,7 +39,7 @@ def fused_gate_attention(
to information from different representation subspaces. This API only to information from different representation subspaces. This API only
support self_attention. The pseudo code is as follows: support self_attention. The pseudo code is as follows:
.. code-block:: python .. code-block:: text
c = c ** (-0.5) c = c ** (-0.5)
q = paddle.einsum('nbqa,ahc->nbqhc', q_data, query_w) * c q = paddle.einsum('nbqa,ahc->nbqhc', q_data, query_w) * c
...@@ -64,20 +64,20 @@ def fused_gate_attention( ...@@ -64,20 +64,20 @@ def fused_gate_attention(
Args: Args:
query (Tensor): The input query tensor. The shape is [batch_size, msa_len, res_len, q_dim]. query (Tensor): The input query tensor. The shape is [batch_size, msa_len, res_len, q_dim].
key (Tensor, optional): The input key tensor, which can be set when key (Tensor, optional): The input key tensor, which can be set when
merge_qkv is False. The shape is [batch_size, msa_len, m_size, kv_dim]. merge_qkv is False. The shape is [batch_size, msa_len, m_size, kv_dim]. Default None.
query_weight (Tensor, optional): The weight of query linear, which query_weight (Tensor, optional): The weight of query linear, which should be set when input
should be set when input key is not None. The shape is [q_dim, num_heads, head_dim]. key is not None. The shape is [q_dim, num_heads, head_dim]. Default None.
key_weight (Tensor, optional): The weight of key linear, which should key_weight (Tensor, optional): The weight of key linear, which should be set when input key
be set when input key is not None. The shape is [kv_dim, num_heads, head_dim]. is not None. The shape is [kv_dim, num_heads, head_dim]. Default None.
value_weight (Tensor, optional): The weight of value linear, which should value_weight (Tensor, optional): The weight of value linear, which should be set when input
be set when input key is not None. The shape is [kv_dim, num_heads, head_dim]. key is not None. The shape is [kv_dim, num_heads, head_dim]. Default None.
qkv_weight (Tensor, optional): The weight of qkv linear, which should qkv_weight (Tensor, optional): The weight of qkv linear, which should be set when merge_qkv
be set when merge_qkv is True. The shape is [3, num_heads, head_dim, q_dim]. is True. The shape is [3, num_heads, head_dim, q_dim]. Default None.
gate_linear_weight (Tensor, optional): The weight of gating linear, gate_linear_weight (Tensor, optional): The weight of gating linear, which should be set when
which should be set when has_gating is True. The shape is [q_dim, num_heads, head_dim]. has_gating is True. The shape is [q_dim, num_heads, head_dim]. Default None.
gate_linear_bias (Tensor, optional): The bias of gating linear, which gate_linear_bias (Tensor, optional): The bias of gating linear, which should be set when
should be set when has_gating is True. The shape is [num_heads, head_dim]. Default None. has_gating is True. The shape is [num_heads, head_dim]. Default None.
out_linear_weight (Tensor, optional): The weight of output linear. The shape is [num_heads, head_dim, q_dim]. out_linear_weight (Tensor, optional): The weight of output linear. The shape is [num_heads, head_dim, q_dim]. Default None.
out_linear_bias (Tensor): The bias of output linear, the shape is [q_dim]. Default None. out_linear_bias (Tensor): The bias of output linear, the shape is [q_dim]. Default None.
nonbatched_bias (Tensor, optional): The extra bias. The shape is [batch_size, 1, num_heads, res_len, m_size]. Default None. nonbatched_bias (Tensor, optional): The extra bias. The shape is [batch_size, 1, num_heads, res_len, m_size]. Default None.
attn_mask (Tensor, optional): The attention mask. The shape is [batch_size, msa_len, 1, 1, res_len]. Default None. attn_mask (Tensor, optional): The attention mask. The shape is [batch_size, msa_len, 1, 1, res_len]. Default None.
...@@ -92,54 +92,54 @@ def fused_gate_attention( ...@@ -92,54 +92,54 @@ def fused_gate_attention(
.. code-block:: python .. code-block:: python
# required: gpu >>> # doctest: +REQUIRES(env:GPU)
import paddle >>> import paddle
import paddle.incubate.nn.functional as F >>> import paddle.incubate.nn.functional as F
# batch_size = 2 >>> # batch_size = 2
# msa_len = 4 >>> # msa_len = 4
# res_len = 2 >>> # res_len = 2
# q_dim = 4 >>> # q_dim = 4
# num_heads = 8 >>> # num_heads = 8
# head_dim = 4 >>> # head_dim = 4
# m_size = res_len (when merge_qkv is True) >>> # m_size = res_len (when merge_qkv is True)
# query: [batch_size, msa_len, res_len, q_dim] >>> # query: [batch_size, msa_len, res_len, q_dim]
query = paddle.rand(shape=[2, 4, 2, 4], dtype="float32") >>> query = paddle.rand(shape=[2, 4, 2, 4], dtype="float32")
# qkv_weight: [3, n_heads, head_dim, q_dim] >>> # qkv_weight: [3, n_heads, head_dim, q_dim]
qkv_weight = paddle.rand(shape=[3, 8, 4, 4], dtype="float32") >>> qkv_weight = paddle.rand(shape=[3, 8, 4, 4], dtype="float32")
# nonbatched_bias: [batch_size, 1, num_heads, res_len, m_size] >>> # nonbatched_bias: [batch_size, 1, num_heads, res_len, m_size]
nonbatched_bias = paddle.rand(shape=[2, 1, 8, 2, 2], dtype="float32") >>> nonbatched_bias = paddle.rand(shape=[2, 1, 8, 2, 2], dtype="float32")
# attn_mask: [batch_size, msa_len, 1, 1, m_size] >>> # attn_mask: [batch_size, msa_len, 1, 1, m_size]
attn_mask = paddle.rand(shape=[2, 4, 1, 1, 2], dtype="float32") >>> attn_mask = paddle.rand(shape=[2, 4, 1, 1, 2], dtype="float32")
# gate_linear_weight: [q_dim, num_heads, head_dim] >>> # gate_linear_weight: [q_dim, num_heads, head_dim]
gate_linear_weight = paddle.rand(shape=[4, 8, 4], dtype="float32") >>> gate_linear_weight = paddle.rand(shape=[4, 8, 4], dtype="float32")
# gate_bias: [num_heads, head_dim] >>> # gate_bias: [num_heads, head_dim]
gate_linear_bias = paddle.rand(shape=[8, 4], dtype="float32") >>> gate_linear_bias = paddle.rand(shape=[8, 4], dtype="float32")
# out_linear_weight: [num_heads, head_dim, q_dim] >>> # out_linear_weight: [num_heads, head_dim, q_dim]
out_linear_weight = paddle.rand(shape=[8, 4, 4], dtype="float32") >>> out_linear_weight = paddle.rand(shape=[8, 4, 4], dtype="float32")
# out_linear_bias: [q_dim] >>> # out_linear_bias: [q_dim]
out_linear_bias = paddle.rand(shape=[4], dtype="float32") >>> out_linear_bias = paddle.rand(shape=[4], dtype="float32")
# output: [batch_size, msa_len, res_len, q_dim] >>> # output: [batch_size, msa_len, res_len, q_dim]
output = F.fused_gate_attention( >>> output = F.fused_gate_attention(
query=query, ... query=query,
qkv_weight=qkv_weight, ... qkv_weight=qkv_weight,
gate_linear_weight=gate_linear_weight, ... gate_linear_weight=gate_linear_weight,
gate_linear_bias=gate_linear_bias, ... gate_linear_bias=gate_linear_bias,
out_linear_weight=out_linear_weight, ... out_linear_weight=out_linear_weight,
out_linear_bias=out_linear_bias, ... out_linear_bias=out_linear_bias,
nonbatched_bias=nonbatched_bias, ... nonbatched_bias=nonbatched_bias,
attn_mask=attn_mask, ... attn_mask=attn_mask,
has_gating=True, ... has_gating=True,
merge_qkv=True) ... merge_qkv=True)
print(output.shape) >>> print(output.shape)
# [2, 4, 2, 4] [2, 4, 2, 4]
""" """
if in_dynamic_mode(): if in_dynamic_mode():
......
python/paddle/incubate/nn/functional/fused_matmul_bias.py
浏览文件 @ 4dbe441c
...@@ -28,11 +28,11 @@ def fused_matmul_bias( ...@@ -28,11 +28,11 @@ def fused_matmul_bias(
Args: Args:
x (Tensor): the first input Tensor to be multiplied. x (Tensor): the first input Tensor to be multiplied.
y (Tensor): the second input Tensor to be multiplied. Its rank must be 2. y (Tensor): the second input Tensor to be multiplied. Its rank must be 2.
bias (Tensor|None): the input bias Tensor. If it is None, no bias addition would bias (Tensor, optional): the input bias Tensor. If it is None, no bias addition would
be performed. Otherwise, the bias is added to the matrix multiplication result. be performed. Otherwise, the bias is added to the matrix multiplication result. Default: None.
transpose_x (bool): Whether to transpose :math:`x` before multiplication. transpose_x (bool, optional): Whether to transpose :math:`x` before multiplication. Default: False.
transpose_y (bool): Whether to transpose :math:`y` before multiplication. transpose_y (bool, optional): Whether to transpose :math:`y` before multiplication. Default: False.
name(str|None): For detailed information, please refer to name (str, optional): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default. :ref:`api_guide_Name` . Usually name is no need to set and None by default.
Returns: Returns:
...@@ -41,15 +41,18 @@ def fused_matmul_bias( ...@@ -41,15 +41,18 @@ def fused_matmul_bias(
Examples: Examples:
.. code-block:: python .. code-block:: python
# required: gpu >>> # doctest: +SKIP('fused_gemm_epilogue is only supported when CUDA version >= 11.6')
import paddle >>> # doctest: +REQUIRES(env:GPU)
from paddle.incubate.nn.functional import fused_matmul_bias >>> import paddle
>>> from paddle.incubate.nn.functional import fused_matmul_bias
x = paddle.randn([3, 4])
y = paddle.randn([4, 5]) >>> paddle.set_device('gpu')
bias = paddle.randn([5]) >>> x = paddle.randn([3, 5])
out = fused_matmul_bias(x, y, bias) >>> y = paddle.randn([4, 5])
print(out.shape) # [3, 5] >>> bias = paddle.randn([5])
>>> out = fused_matmul_bias(x, y, bias)
>>> print(out.shape)
[3, 5]
""" """
if bias is None: if bias is None:
return matmul(x, y, transpose_x, transpose_y, name) return matmul(x, y, transpose_x, transpose_y, name)
...@@ -76,10 +79,10 @@ def fused_linear(x, weight, bias=None, transpose_weight=False, name=None): ...@@ -76,10 +79,10 @@ def fused_linear(x, weight, bias=None, transpose_weight=False, name=None):
Args: Args:
x (Tensor): the input Tensor to be multiplied. x (Tensor): the input Tensor to be multiplied.
weight (Tensor): the weight Tensor to be multiplied. Its rank must be 2. weight (Tensor): the weight Tensor to be multiplied. Its rank must be 2.
bias (Tensor|None): the input bias Tensor. If it is None, no bias addition would bias (Tensor, optional): the input bias Tensor. If it is None, no bias addition would
be performed. Otherwise, the bias is added to the matrix multiplication result. be performed. Otherwise, the bias is added to the matrix multiplication result. Default: None.
transpose_weight (bool): Whether to transpose :math:`weight` before multiplication. transpose_weight (bool, optional): Whether to transpose :math:`weight` before multiplication. Default: False.
name(str|None): For detailed information, please refer to name (str, optional): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default. :ref:`api_guide_Name` . Usually name is no need to set and None by default.
Returns: Returns:
...@@ -88,15 +91,18 @@ def fused_linear(x, weight, bias=None, transpose_weight=False, name=None): ...@@ -88,15 +91,18 @@ def fused_linear(x, weight, bias=None, transpose_weight=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
# required: gpu >>> # doctest: +SKIP('fused_gemm_epilogue is only supported when CUDA version >= 11.6')
import paddle >>> # doctest: +REQUIRES(env:GPU)
from paddle.incubate.nn.functional import fused_linear >>> import paddle
>>> from paddle.incubate.nn.functional import fused_linear
x = paddle.randn([3, 4])
weight = paddle.randn([4, 5]) >>> paddle.set_device('gpu')
bias = paddle.randn([5]) >>> x = paddle.randn([3, 4])
out = fused_linear(x, weight, bias) >>> weight = paddle.randn([4, 5])
print(out.shape) # [3, 5] >>> bias = paddle.randn([5])
>>> out = fused_linear(x, weight, bias)
>>> print(out.shape)
[3, 5]
""" """
return fused_matmul_bias(x, weight, bias, False, transpose_weight, name) return fused_matmul_bias(x, weight, bias, False, transpose_weight, name)
...@@ -109,25 +115,32 @@ def fused_linear_activation( ...@@ -109,25 +115,32 @@ def fused_linear_activation(
Args: Args:
x (Tensor): the input Tensor to be multiplied. x (Tensor): the input Tensor to be multiplied.
weight (Tensor): the weight Tensor to be multiplied. Its rank must be 2. y (Tensor): the weight Tensor to be multiplied. Its rank must be 2.
bias (Tensor): the input bias Tensor, the bias is added to the matrix multiplication result. bias (Tensor): the input bias Tensor, the bias is added to the matrix multiplication result.
transpose_weight (bool): Whether to transpose :math:`weight` before multiplication. trans_x (bool, optional): Whether to transpose :math:`x` before multiplication.
activation(str|None): Activation function, Currently, the available activation functions are limited to "gelu" (Gaussian Error Linear Unit) and "relu" (Rectified Linear Unit). These activation functions are applied to the output of the bias add. trans_y (bool, optional): Whether to transpose :math:`y` before multiplication.
activation (str, optional): Activation function, Currently, the available activation functions are
limited to "gelu" (Gaussian Error Linear Unit) and "relu" (Rectified Linear Unit).
These activation functions are applied to the output of the bias add. Default: None.
Returns: Returns:
Tensor: the output Tensor. Tensor: the output Tensor.
Examples: Examples:
.. code-block:: python .. code-block:: python
# required: gpu >>> # doctest: +SKIP('fused_gemm_epilogue is only supported when CUDA version >= 11.6')
import paddle >>> # doctest: +REQUIRES(env:GPU)
from paddle.incubate.nn.functional import fused_linear_activation >>> import paddle
>>> from paddle.incubate.nn.functional import fused_linear_activation
x = paddle.randn([3, 4])
weight = paddle.randn([4, 5]) >>> paddle.set_device('gpu')
bias = paddle.randn([5]) >>> x = paddle.randn([3, 4])
out = fused_linear_activation(x, weight, bias) >>> weight = paddle.randn([4, 5])
print(out.shape) # [3, 5] >>> bias = paddle.randn([5])
>>> out = fused_linear_activation(x, weight, bias)
>>> print(out.shape)
[3, 5]
""" """
if activation is None: if activation is None:
activation = "none" activation = "none"
......
python/paddle/incubate/nn/functional/fused_rotary_position_embedding.py
浏览文件 @ 4dbe441c
...@@ -44,14 +44,13 @@ def fused_rotary_position_embedding( ...@@ -44,14 +44,13 @@ def fused_rotary_position_embedding(
Examples: Examples:
.. code-block:: python .. code-block:: python
>>> # required: gpu
>>> # doctest: +REQUIRES(env:GPU) >>> # doctest: +REQUIRES(env:GPU)
>>> import paddle >>> import paddle
>>> from paddle.incubate.nn.functional import fused_rotary_position_embedding >>> from paddle.incubate.nn.functional import fused_rotary_position_embedding
>>> paddle.device.set_device('gpu') >>> paddle.set_device('gpu')
>>> # batch_size = 2 >>> # batch_size = 2
>>> # seq_len = 2 >>> # seq_len = 2
......
python/paddle/tensor/linalg.py
浏览文件 @ 4dbe441c
...@@ -45,44 +45,44 @@ def transpose(x, perm, name=None): ...@@ -45,44 +45,44 @@ def transpose(x, perm, name=None):
Args: Args:
x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32. x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32.
perm (list|tuple): Permute the input according to the data of perm. perm (list|tuple): Permute the input according to the data of perm.
name (str): The name of this layer. It is optional. name (str, optional): The name of this layer. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns: Returns:
Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64. Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64.
For Example: Examples:
.. code-block:: text .. code-block:: text
x = [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] x = [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]]
[[13 14 15 16] [17 18 19 20] [21 22 23 24]]] [[13 14 15 16] [17 18 19 20] [21 22 23 24]]]
shape(x) = [2,3,4] shape(x) = [2,3,4]
# Example 1 # Example 1
perm0 = [1,0,2] perm0 = [1,0,2]
y_perm0 = [[[ 1 2 3 4] [13 14 15 16]] y_perm0 = [[[ 1 2 3 4] [13 14 15 16]]
[[ 5 6 7 8] [17 18 19 20]] [[ 5 6 7 8] [17 18 19 20]]
[[ 9 10 11 12] [21 22 23 24]]] [[ 9 10 11 12] [21 22 23 24]]]
shape(y_perm0) = [3,2,4] shape(y_perm0) = [3,2,4]
# Example 2 # Example 2
perm1 = [2,1,0] perm1 = [2,1,0]
y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]] y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]]
[[ 2 14] [ 6 18] [10 22]] [[ 2 14] [ 6 18] [10 22]]
[[ 3 15] [ 7 19] [11 23]] [[ 3 15] [ 7 19] [11 23]]
[[ 4 16] [ 8 20] [12 24]]] [[ 4 16] [ 8 20] [12 24]]]
shape(y_perm1) = [4,3,2] shape(y_perm1) = [4,3,2]
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.randn([2, 3, 4]) >>> x = paddle.randn([2, 3, 4])
x_transposed = paddle.transpose(x, perm=[1, 0, 2]) >>> x_transposed = paddle.transpose(x, perm=[1, 0, 2])
print(x_transposed.shape) >>> print(x_transposed.shape)
# [3L, 2L, 4L] [3, 2, 4]
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -180,10 +180,9 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None): ...@@ -180,10 +180,9 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
Args: Args:
x (Tensor): The input tensor which is a Tensor. x (Tensor): The input tensor which is a Tensor.
y (Tensor): The input tensor which is a Tensor. y (Tensor): The input tensor which is a Tensor.
transpose_x (bool, optional): Whether to transpose :math:`x` before multiplication. transpose_x (bool, optional): Whether to transpose :math:`x` before multiplication. Default is False.
transpose_y (bool, optional): Whether to transpose :math:`y` before multiplication. transpose_y (bool, optional): Whether to transpose :math:`y` before multiplication. Default is False.
name(str, optional): A name for this layer(optional). If set None, the layer name (str, optional): If set None, the layer will be named automatically. For more information, please refer to :ref:`api_guide_Name`. Default is None.
will be named automatically.
Returns: Returns:
Tensor: The output Tensor. Tensor: The output Tensor.
...@@ -192,42 +191,42 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None): ...@@ -192,42 +191,42 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
# vector * vector >>> # vector * vector
x = paddle.rand([10]) >>> x = paddle.rand([10])
y = paddle.rand([10]) >>> y = paddle.rand([10])
z = paddle.matmul(x, y) >>> z = paddle.matmul(x, y)
print(z.shape) >>> print(z.shape)
# () []
# matrix * vector >>> # matrix * vector
x = paddle.rand([10, 5]) >>> x = paddle.rand([10, 5])
y = paddle.rand([5]) >>> y = paddle.rand([5])
z = paddle.matmul(x, y) >>> z = paddle.matmul(x, y)
print(z.shape) >>> print(z.shape)
# (10,) [10]
# batched matrix * broadcasted vector >>> # batched matrix * broadcasted vector
x = paddle.rand([10, 5, 2]) >>> x = paddle.rand([10, 5, 2])
y = paddle.rand([2]) >>> y = paddle.rand([2])
z = paddle.matmul(x, y) >>> z = paddle.matmul(x, y)
print(z.shape) >>> print(z.shape)
# (10, 5) [10, 5]
# batched matrix * batched matrix >>> # batched matrix * batched matrix
x = paddle.rand([10, 5, 2]) >>> x = paddle.rand([10, 5, 2])
y = paddle.rand([10, 2, 5]) >>> y = paddle.rand([10, 2, 5])
z = paddle.matmul(x, y) >>> z = paddle.matmul(x, y)
print(z.shape) >>> print(z.shape)
# (10, 5, 5) [10, 5, 5]
# batched matrix * broadcasted matrix >>> # batched matrix * broadcasted matrix
x = paddle.rand([10, 1, 5, 2]) >>> x = paddle.rand([10, 1, 5, 2])
y = paddle.rand([1, 3, 2, 5]) >>> y = paddle.rand([1, 3, 2, 5])
z = paddle.matmul(x, y) >>> z = paddle.matmul(x, y)
print(z.shape) >>> print(z.shape)
# (10, 3, 5, 5) [10, 3, 5, 5]
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -305,54 +304,61 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None): ...@@ -305,54 +304,61 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12 >>> x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12
# x: Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, >>> print(x)
# [[[-12., -11., -10., -9. ], Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
# [-8. , -7. , -6. , -5. ], [[[-12., -11., -10., -9. ],
# [-4. , -3. , -2. , -1. ]], [-8. , -7. , -6. , -5. ],
[-4. , -3. , -2. , -1. ]],
# [[ 0. , 1. , 2. , 3. ], [[ 0. , 1. , 2. , 3. ],
# [ 4. , 5. , 6. , 7. ], [ 4. , 5. , 6. , 7. ],
# [ 8. , 9. , 10., 11.]]]) [ 8. , 9. , 10., 11.]]])
# compute frobenius norm along last two dimensions. >>> # compute frobenius norm along last two dimensions.
out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1]) >>> out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])
# out_fro: Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, >>> print(out_fro)
# [17.43559647, 16.91153526, 16.73320007, 16.91153526]) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[17.43559647, 16.91153526, 16.73320007, 16.91153526])
# compute 2-order vector norm along last dimension.
out_pnorm = paddle.linalg.norm(x, p=2, axis=-1) >>> # compute 2-order vector norm along last dimension.
# out_pnorm: Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, >>> out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)
# [[21.11871147, 13.19090557, 5.47722578 ], >>> print(out_pnorm)
# [3.74165750 , 11.22497177, 19.13112640]]) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[21.11871147, 13.19090557, 5.47722578 ],
# compute 2-order norm along [0,1] dimension. [3.74165750 , 11.22497177, 19.13112640]])
out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])
# out_pnorm: Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, >>> # compute 2-order norm along [0,1] dimension.
# [17.43559647, 16.91153526, 16.73320007, 16.91153526]) >>> out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])
>>> print(out_pnorm)
# compute inf-order norm Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
out_pnorm = paddle.linalg.norm(x, p=float("inf")) [17.43559647, 16.91153526, 16.73320007, 16.91153526])
# out_pnorm = Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 12.) >>> # compute inf-order norm
>>> out_pnorm = paddle.linalg.norm(x, p=float("inf"))
out_pnorm = paddle.linalg.norm(x, p=float("inf"), axis=0) >>> print(out_pnorm)
# out_pnorm: Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[12., 11., 10., 9. ], 12.)
# [8. , 7. , 6. , 7. ],
# [8. , 9. , 10., 11.]]) >>> out_pnorm = paddle.linalg.norm(x, p=float("inf"), axis=0)
>>> print(out_pnorm)
# compute -inf-order norm Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
out_pnorm = paddle.linalg.norm(x, p=-float("inf")) [[12., 11., 10., 9. ],
# out_pnorm: Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, [8. , 7. , 6. , 7. ],
# 0.) [8. , 9. , 10., 11.]])
out_pnorm = paddle.linalg.norm(x, p=-float("inf"), axis=0) >>> # compute -inf-order norm
# out_pnorm: Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, >>> out_pnorm = paddle.linalg.norm(x, p=-float("inf"))
# [[0., 1., 2., 3.], >>> print(out_pnorm)
# [4., 5., 6., 5.], Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# [4., 3., 2., 1.]]) 0.)
>>> out_pnorm = paddle.linalg.norm(x, p=-float("inf"), axis=0)
>>> print(out_pnorm)
Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0., 1., 2., 3.],
[4., 5., 6., 5.],
[4., 3., 2., 1.]])
""" """
def frobenius_norm(input, dim=None, keepdim=False, name=None): def frobenius_norm(input, dim=None, keepdim=False, name=None):
...@@ -360,8 +366,10 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None): ...@@ -360,8 +366,10 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`. The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.
Args: Args:
input (Variable): Tensor, data type float32, float64. input (Variable): Tensor, data type float32, float64.
dim (list, optional): None for last two dimensions. dim (list, optional): None for last two dimensions. Default None.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False. keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
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`.
""" """
if dim is not None and not (isinstance(dim, list) and len(dim) == 2): if dim is not None and not (isinstance(dim, list) and len(dim) == 2):
raise ValueError( raise ValueError(
...@@ -400,9 +408,12 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None): ...@@ -400,9 +408,12 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
Calculate the p-order vector norm for certain dimension of Tensor `input`. Calculate the p-order vector norm for certain dimension of Tensor `input`.
Args: Args:
input (Variable): Tensor, data type float32, float64. input (Variable): Tensor, data type float32, float64.
porder (float, optional): None for porder=2.0. porder (float, optional): None for porder=2.0. Default None.
axis (int, optional): None for last dimension. axis (int, optional): None for last dimension. Default None.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False. keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.
asvector (bool, optional): Whether keep the result as a vector, Default False.
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`.
""" """
if in_dynamic_mode(): if in_dynamic_mode():
if axis is None: if axis is None:
...@@ -682,21 +693,29 @@ def dist(x, y, p=2, name=None): ...@@ -682,21 +693,29 @@ def dist(x, y, p=2, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[3, 3],[3, 3]], dtype="float32") >>> x = paddle.to_tensor([[3, 3],[3, 3]], dtype="float32")
y = paddle.to_tensor([[3, 3],[3, 1]], dtype="float32") >>> y = paddle.to_tensor([[3, 3],[3, 1]], dtype="float32")
out = paddle.dist(x, y, 0) >>> out = paddle.dist(x, y, 0)
print(out) # out = 1. >>> print(out)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
out = paddle.dist(x, y, 2) 1.)
print(out) # out = 2.
>>> out = paddle.dist(x, y, 2)
out = paddle.dist(x, y, float("inf")) >>> print(out)
print(out) # out = 2. Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
2.)
out = paddle.dist(x, y, float("-inf"))
print(out) # out = 0. >>> out = paddle.dist(x, y, float("inf"))
>>> print(out)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
2.)
>>> out = paddle.dist(x, y, float("-inf"))
>>> print(out)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.)
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.dist(x, y, p) return _C_ops.dist(x, y, p)
...@@ -740,83 +759,95 @@ def cond(x, p=None, name=None): ...@@ -740,83 +759,95 @@ def cond(x, p=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]]) >>> x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])
# compute conditional number when p is None >>> # compute conditional number when p is None
out = paddle.linalg.cond(x) >>> out = paddle.linalg.cond(x)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, >>> print(out)
# 1.41421342) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.41421378)
# compute conditional number when order of the norm is 'fro'
out_fro = paddle.linalg.cond(x, p='fro') >>> # compute conditional number when order of the norm is 'fro'
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, >>> out_fro = paddle.linalg.cond(x, p='fro')
# 3.16227770) >>> print(out_fro)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# compute conditional number when order of the norm is 'nuc' 3.16227770)
out_nuc = paddle.linalg.cond(x, p='nuc')
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, >>> # compute conditional number when order of the norm is 'nuc'
# 9.24263859) >>> out_nuc = paddle.linalg.cond(x, p='nuc')
>>> print(out_nuc)
# compute conditional number when order of the norm is 1 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
out_1 = paddle.linalg.cond(x, p=1) 9.24264145)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 2.) >>> # compute conditional number when order of the norm is 1
>>> out_1 = paddle.linalg.cond(x, p=1)
# compute conditional number when order of the norm is -1 >>> print(out_1)
out_minus_1 = paddle.linalg.cond(x, p=-1) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, 2.)
# 1.)
>>> # compute conditional number when order of the norm is -1
# compute conditional number when order of the norm is 2 >>> out_minus_1 = paddle.linalg.cond(x, p=-1)
out_2 = paddle.linalg.cond(x, p=2) >>> print(out_minus_1)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 1.41421342) 1.)
# compute conditional number when order of the norm is -1 >>> # compute conditional number when order of the norm is 2
out_minus_2 = paddle.linalg.cond(x, p=-2) >>> out_2 = paddle.linalg.cond(x, p=2)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, >>> print(out_2)
# 0.70710683) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.41421378)
# compute conditional number when order of the norm is inf
out_inf = paddle.linalg.cond(x, p=float("inf")) >>> # compute conditional number when order of the norm is -1
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, >>> out_minus_2 = paddle.linalg.cond(x, p=-2)
# 2.) >>> print(out_minus_2)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# compute conditional number when order of the norm is -inf 0.70710671)
out_minus_inf = paddle.linalg.cond(x, p=-float("inf"))
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True, >>> # compute conditional number when order of the norm is inf
# 1.) >>> out_inf = paddle.linalg.cond(x, p=float("inf"))
>>> print(out_inf)
a = paddle.randn([2, 4, 4]) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# Tensor(shape=[2, 4, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True, 2.)
# [[[-0.06784091, -0.07095790, 1.31792855, -0.58959651],
# [ 0.20818676, -0.85640615, -0.89998871, -1.47439921], >>> # compute conditional number when order of the norm is -inf
# [-0.49132481, 0.42250812, -0.77383220, -2.19794774], >>> out_minus_inf = paddle.linalg.cond(x, p=-float("inf"))
# [-0.33551720, -1.70003879, -1.09795380, -0.63737559]], >>> print(out_minus_inf)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[ 1.12026262, -0.16119350, -1.21157813, 2.74383283], 1.)
# [-0.15999718, 0.18798758, -0.69392562, 1.35720372],
# [-0.53013402, -2.26304483, 1.40843511, -1.02288902], >>> a = paddle.randn([2, 4, 4])
# [ 0.69533503, 2.05261683, -0.02251151, -1.43127477]]]) >>> print(a)
Tensor(shape=[2, 4, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
a_cond_fro = paddle.linalg.cond(a, p='fro') [[[ 0.06132207, 1.11349595, 0.41906244, -0.24858207],
# Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [-1.85169315, -1.50370061, 1.73954511, 0.13331604],
# [8.86691189 , 75.23817444]) [ 1.66359663, -0.55764782, -0.59911072, -0.57773495],
[-1.03176904, -0.33741450, -0.29695082, -1.50258386]],
b = paddle.randn([2, 3, 4]) [[ 0.67233968, -1.07747352, 0.80170447, -0.06695852],
# Tensor(shape=[2, 3, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True, [-1.85003340, -0.23008066, 0.65083790, 0.75387722],
# [[[-0.43754861, 1.80796063, -0.78729683, -1.82264030], [ 0.61212337, -0.52664012, 0.19209868, -0.18707706],
# [-0.27670753, 0.06620564, 0.29072434, -0.31155765], [-0.00711021, 0.35236868, -0.40404350, 1.28656745]]])
# [ 0.34123746, -0.05444612, 0.05001324, -1.46877074]],
>>> a_cond_fro = paddle.linalg.cond(a, p='fro')
# [[-0.64331555, -1.51103854, -1.26277697, -0.68024760], >>> print(a_cond_fro)
# [ 2.59375715, -1.06665540, 0.96575671, -0.73330832], Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
# [-0.47064447, -0.23945692, -0.95150250, -1.07125998]]]) [6.37173700 , 35.15114594])
b_cond_2 = paddle.linalg.cond(b, p=2)
# Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True, >>> b = paddle.randn([2, 3, 4])
# [6.64228773, 3.89068866]) >>> print(b)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.03306439, 0.70149767, 0.77064633, -0.55978841],
[-0.84461296, 0.99335045, -1.23486686, 0.59551388],
[-0.63035583, -0.98797107, 0.09410731, 0.47007179]],
[[ 0.85850012, -0.98949534, -1.63086998, 1.07340240],
[-0.05492965, 1.04750168, -2.33754158, 1.16518629],
[ 0.66847134, -1.05326962, -0.05703246, -0.48190674]]])
>>> b_cond_2 = paddle.linalg.cond(b, p=2)
>>> print(b_cond_2)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[2.86566353, 6.85834455])
""" """
...@@ -1081,7 +1112,7 @@ def dot(x, y, name=None): ...@@ -1081,7 +1112,7 @@ def dot(x, y, name=None):
Parameters: Parameters:
x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``, ``complex64``, ``complex128`` x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``, ``complex64``, ``complex128``
y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``, ``complex64``, ``complex128`` y(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``, ``complex64``, ``complex128``
name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`
Returns: Returns:
...@@ -1089,21 +1120,25 @@ def dot(x, y, name=None): ...@@ -1089,21 +1120,25 @@ def dot(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
# 1-D Tensor * 1-D Tensor >>> # 1-D Tensor * 1-D Tensor
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([4, 5, 6]) >>> y = paddle.to_tensor([4, 5, 6])
z = paddle.dot(x, y) >>> z = paddle.dot(x, y)
print(z) # 32 >>> print(z)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
32)
# 2-D Tensor * 2-D Tensor >>> # 2-D Tensor * 2-D Tensor
x = paddle.to_tensor([[1, 2, 3], [2, 4, 6]]) >>> x = paddle.to_tensor([[1, 2, 3], [2, 4, 6]])
y = paddle.to_tensor([[4, 5, 6], [4, 5, 6]]) >>> y = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
z = paddle.dot(x, y) >>> z = paddle.dot(x, y)
print(z) # [32, 64] >>> print(z)
Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True,
[32, 64])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -1167,31 +1202,30 @@ def cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None): ...@@ -1167,31 +1202,30 @@ def cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):
element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself. element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.
Parameters: Parameters:
x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below. x (Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.
rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True rowvar (Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True.
ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True ddof (Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True.
fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None fweights (Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None.
aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None aweights (Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None.
name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` name (str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` .
Returns: Returns:
Tensor: The covariance matrix Tensor of the variables. Tensor: The covariance matrix Tensor of the variables.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle
xt = paddle.rand((3, 4)) >>> import paddle
paddle.linalg.cov(xt) >>> paddle.seed(2023)
''' >>> xt = paddle.rand((3, 4))
Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True, >>> paddle.linalg.cov(xt)
[[0.07918842, 0.06127326, 0.01493049], >>> print(xt)
[0.06127326, 0.06166256, 0.00302668], Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.01493049, 0.00302668, 0.01632146]]) [[0.86583614, 0.52014720, 0.25960937, 0.90525323],
''' [0.42400089, 0.40641287, 0.97020894, 0.74437362],
[0.51785129, 0.73292869, 0.97786582, 0.04315904]])
""" """
op_type = 'cov' op_type = 'cov'
if len(x.shape) > 2 or len(x.shape) < 1: if len(x.shape) > 2 or len(x.shape) < 1:
...@@ -1289,35 +1323,48 @@ def t(input, name=None): ...@@ -1289,35 +1323,48 @@ def t(input, name=None):
Args: Args:
input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float32, float64, int32, int64. input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no need for 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` user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Returns: Returns:
Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64. Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.
Examples: Examples:
.. code-block:: python .. code-block:: python
:name: code-example :name: code-example
import paddle
>>> import paddle
# Example 1 (0-D tensor)
x = paddle.to_tensor([0.79]) >>> # Example 1 (0-D tensor)
paddle.t(x) # [0.79] >>> x = paddle.to_tensor([0.79])
>>> out = paddle.t(x)
# Example 2 (1-D tensor) >>> print(out)
x = paddle.to_tensor([0.79, 0.84, 0.32]) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
paddle.t(x) # [0.79000002, 0.83999997, 0.31999999] [0.79000002])
paddle.t(x).shape # [3]
>>> # Example 2 (1-D tensor)
# Example 3 (2-D tensor) >>> x = paddle.to_tensor([0.79, 0.84, 0.32])
x = paddle.to_tensor([[0.79, 0.84, 0.32], >>> out2 = paddle.t(x)
[0.64, 0.14, 0.57]]) >>> print(out2)
x.shape # [2, 3] Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
paddle.t(x) [0.79000002, 0.83999997, 0.31999999])
# [[0.79000002, 0.63999999], >>> print(paddle.t(x).shape)
# [0.83999997, 0.14000000], [3]
# [0.31999999, 0.56999999]]
paddle.t(x).shape # [3, 2] >>> # Example 3 (2-D tensor)
>>> x = paddle.to_tensor([[0.79, 0.84, 0.32],
... [0.64, 0.14, 0.57]])
>>> print(x.shape)
[2, 3]
>>> out3 = paddle.t(x)
>>> print(out3)
Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.79000002, 0.63999999],
[0.83999997, 0.14000000],
[0.31999999, 0.56999999]])
>>> print(paddle.t(x).shape)
[3, 2]
""" """
if len(input.shape) > 2: if len(input.shape) > 2:
...@@ -1375,24 +1422,28 @@ def cross(x, y, axis=9, name=None): ...@@ -1375,24 +1422,28 @@ def cross(x, y, axis=9, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1.0, 1.0, 1.0], >>> x = paddle.to_tensor([[1.0, 1.0, 1.0],
[2.0, 2.0, 2.0], ... [2.0, 2.0, 2.0],
[3.0, 3.0, 3.0]]) ... [3.0, 3.0, 3.0]])
y = paddle.to_tensor([[1.0, 1.0, 1.0], >>> y = paddle.to_tensor([[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], ... [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]]) ... [1.0, 1.0, 1.0]])
...
z1 = paddle.cross(x, y) >>> z1 = paddle.cross(x, y)
# [[-1. -1. -1.] >>> print(z1)
# [ 2. 2. 2.] Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [-1. -1. -1.]] [[-1., -1., -1.],
[ 2., 2., 2.],
z2 = paddle.cross(x, y, axis=1) [-1., -1., -1.]])
# [[0. 0. 0.]
# [0. 0. 0.] >>> z2 = paddle.cross(x, y, axis=1)
# [0. 0. 0.]] >>> print(z2)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
axis = K_DEFAULT_DIM if axis is None else axis axis = K_DEFAULT_DIM if axis is None else axis
...@@ -1439,7 +1490,7 @@ def cholesky(x, upper=False, name=None): ...@@ -1439,7 +1490,7 @@ def cholesky(x, upper=False, name=None):
where * is zero or more batch dimensions, and matrices on the where * is zero or more batch dimensions, and matrices on the
inner-most 2 dimensions all should be symmetric positive-definite. inner-most 2 dimensions all should be symmetric positive-definite.
Its data type should be float32 or float64. Its data type should be float32 or float64.
upper (bool): The flag indicating whether to return upper or lower upper (bool, optional): The flag indicating whether to return upper or lower
triangular matrices. Default: False. triangular matrices. Default: False.
name (str, optional): Name for the operation (optional, default is None). name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`.
...@@ -1451,14 +1502,19 @@ def cholesky(x, upper=False, name=None): ...@@ -1451,14 +1502,19 @@ def cholesky(x, upper=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
a = paddle.rand([3, 3], dtype="float32") >>> a = paddle.rand([3, 3], dtype="float32")
a_t = paddle.transpose(a, [1, 0]) >>> a_t = paddle.transpose(a, [1, 0])
x = paddle.matmul(a, a_t) + 1e-03 >>> x = paddle.matmul(a, a_t) + 1e-03
out = paddle.linalg.cholesky(x, upper=False) >>> out = paddle.linalg.cholesky(x, upper=False)
print(out) >>> print(out)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1.04337072, 0. , 0. ],
[1.06467664, 0.17859250, 0. ],
[1.30602181, 0.08326444, 0.22790681]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.cholesky(x, upper) return _C_ops.cholesky(x, upper)
...@@ -1486,12 +1542,12 @@ def matrix_rank(x, tol=None, hermitian=False, name=None): ...@@ -1486,12 +1542,12 @@ def matrix_rank(x, tol=None, hermitian=False, name=None):
Args: Args:
x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch
of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64. of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.
tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest tol (float|Tensor, optional): the tolerance value. If `tol` is not specified, and `sigma` is the largest singular value
singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed with formula
with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch. `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch. Default: None.
hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian, hermitian (bool, optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,
enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use
the lower triangular of the matrix to compute. the lower triangular of the matrix to compute. Default: False.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -1500,19 +1556,21 @@ def matrix_rank(x, tol=None, hermitian=False, name=None): ...@@ -1500,19 +1556,21 @@ def matrix_rank(x, tol=None, hermitian=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
a = paddle.eye(10) >>> a = paddle.eye(10)
b = paddle.linalg.matrix_rank(a) >>> b = paddle.linalg.matrix_rank(a)
print(b) >>> print(b)
# b = 10 Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True,
10)
c = paddle.ones(shape=[3, 4, 5, 5]) >>> c = paddle.ones(shape=[3, 4, 5, 5])
d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True) >>> d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)
print(d) >>> print(d)
# d = [[1, 1, 1, 1], Tensor(shape=[3, 4], dtype=int32, place=Place(cpu), stop_gradient=True,
# [1, 1, 1, 1], [[1, 1, 1, 1],
# [1, 1, 1, 1]] [1, 1, 1, 1],
[1, 1, 1, 1]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -1567,13 +1625,13 @@ def bmm(x, y, name=None): ...@@ -1567,13 +1625,13 @@ def bmm(x, y, name=None):
Both of the two input tensors must be three-dementional and share the same batch size. Both of the two input tensors must be three-dementional and share the same batch size.
if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor. If x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.
Args: Args:
x (Tensor): The input Tensor. x (Tensor): The input Tensor.
y (Tensor): The input Tensor. y (Tensor): The input Tensor.
name(str|None): A name for this layer(optional). If set None, the layer name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. will be named automatically. Default: None.
Returns: Returns:
Tensor: The product Tensor. Tensor: The product Tensor.
...@@ -1581,23 +1639,23 @@ def bmm(x, y, name=None): ...@@ -1581,23 +1639,23 @@ def bmm(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
# In imperative mode: >>> # In imperative mode:
# size x: (2, 2, 3) and y: (2, 3, 2) >>> # size x: (2, 2, 3) and y: (2, 3, 2)
x = paddle.to_tensor([[[1.0, 1.0, 1.0], >>> x = paddle.to_tensor([[[1.0, 1.0, 1.0],
[2.0, 2.0, 2.0]], ... [2.0, 2.0, 2.0]],
[[3.0, 3.0, 3.0], ... [[3.0, 3.0, 3.0],
[4.0, 4.0, 4.0]]]) ... [4.0, 4.0, 4.0]]])
y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]], >>> y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],
[[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]]) ... [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])
out = paddle.bmm(x, y) >>> out = paddle.bmm(x, y)
# Tensor(shape=[2, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, >>> print(out)
# [[[6. , 6. ], Tensor(shape=[2, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
# [12., 12.]], [[[6. , 6. ],
[12., 12.]],
# [[45., 45.], [[45., 45.],
# [60., 60.]]]) [60., 60.]]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -1639,9 +1697,9 @@ def histogram(input, bins=100, min=0, max=0, name=None): ...@@ -1639,9 +1697,9 @@ def histogram(input, bins=100, min=0, max=0, name=None):
Args: Args:
input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor
should be float32, float64, int32, int64. should be float32, float64, int32, int64.
bins (int, optional): number of histogram bins. bins (int, optional): number of histogram bins. Default: 100.
min (int, optional): lower end of the range (inclusive). min (int, optional): lower end of the range (inclusive). Default: 0.
max (int, optional): upper end of the range (inclusive). max (int, optional): upper end of the range (inclusive). Default: 0.
name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Returns: Returns:
...@@ -1650,11 +1708,13 @@ def histogram(input, bins=100, min=0, max=0, name=None): ...@@ -1650,11 +1708,13 @@ def histogram(input, bins=100, min=0, max=0, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
inputs = paddle.to_tensor([1, 2, 1]) >>> inputs = paddle.to_tensor([1, 2, 1])
result = paddle.histogram(inputs, bins=4, min=0, max=3) >>> result = paddle.histogram(inputs, bins=4, min=0, max=3)
print(result) # [0, 2, 1, 0] >>> print(result)
Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 2, 1, 0])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.histogram(input, bins, min, max) return _C_ops.histogram(input, bins, min, max)
...@@ -1681,8 +1741,8 @@ def bincount(x, weights=None, minlength=0, name=None): ...@@ -1681,8 +1741,8 @@ def bincount(x, weights=None, minlength=0, name=None):
x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor. x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.
weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None. weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.
minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0. minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.
name(str, optional): The default value is None. Normally there is no need for user to set this name (str, optional): Normally there is no need for user to set this property.
property. For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns: Returns:
Tensor: The tensor of frequency. Tensor: The tensor of frequency.
...@@ -1690,15 +1750,19 @@ def bincount(x, weights=None, minlength=0, name=None): ...@@ -1690,15 +1750,19 @@ def bincount(x, weights=None, minlength=0, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 1, 4, 5]) >>> x = paddle.to_tensor([1, 2, 1, 4, 5])
result1 = paddle.bincount(x) >>> result1 = paddle.bincount(x)
print(result1) # [0, 2, 1, 0, 1, 1] >>> print(result1)
Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 2, 1, 0, 1, 1])
w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5]) >>> w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])
result2 = paddle.bincount(x, weights=w) >>> result2 = paddle.bincount(x, weights=w)
print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000] >>> print(result2)
Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 2.19999981, 0.40000001, 0. , 0.50000000, 0.50000000])
""" """
if x.dtype not in [paddle.int32, paddle.int64]: if x.dtype not in [paddle.int32, paddle.int64]:
raise TypeError("Elements in Input(x) should all be integers") raise TypeError("Elements in Input(x) should all be integers")
...@@ -1738,8 +1802,8 @@ def mv(x, vec, name=None): ...@@ -1738,8 +1802,8 @@ def mv(x, vec, name=None):
should be one of float32, float64. should be one of float32, float64.
vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x
should be one of float32, float64. should be one of float32, float64.
name(str, optional): The default value is None. Normally there is no need for user to set this name (str, optional): Normally there is no need for user to set this property.
property. For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns: Returns:
Tensor: The tensor which is producted by x and vec. Tensor: The tensor which is producted by x and vec.
...@@ -1747,17 +1811,17 @@ def mv(x, vec, name=None): ...@@ -1747,17 +1811,17 @@ def mv(x, vec, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
# x: [M, N], vec: [N] >>> # x: [M, N], vec: [N]
# paddle.mv(x, vec) # out: [M] >>> # paddle.mv(x, vec) # out: [M]
import paddle >>> import paddle
x = paddle.to_tensor([[2, 1, 3], [3, 0, 1]]).astype("float64") >>> x = paddle.to_tensor([[2, 1, 3], [3, 0, 1]]).astype("float64")
vec = paddle.to_tensor([3, 5, 1]).astype("float64") >>> vec = paddle.to_tensor([3, 5, 1]).astype("float64")
out = paddle.mv(x, vec) >>> out = paddle.mv(x, vec)
print(out) >>> print(out)
# Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True, Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [14., 10.]) [14., 10.])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.mv(x, vec) return _C_ops.mv(x, vec)
...@@ -1803,8 +1867,8 @@ def det(x, name=None): ...@@ -1803,8 +1867,8 @@ def det(x, name=None):
x (Tensor): the input matrix of size `(n, n)` or the x (Tensor): the input matrix of size `(n, n)` or the
batch of matrices of size `(*, n, n)` where `*` is one or more batch of matrices of size `(*, n, n)` where `*` is one or more
batch dimensions. batch dimensions.
name(str, optional): Name of the output. Default is None. It's used name (str, optional): Name of the output.It's used to print debug info for
to print debug info for developers. Details: :ref:`api_guide_Name` developers. Details: :ref:`api_guide_Name`. Default is None.
Returns: Returns:
Tensor, the determinant value of a square matrix or batches of square matrices. Tensor, the determinant value of a square matrix or batches of square matrices.
...@@ -1812,15 +1876,13 @@ def det(x, name=None): ...@@ -1812,15 +1876,13 @@ def det(x, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
x = paddle.randn([3,3,3]) >>> x = paddle.randn([3,3,3])
>>> A = paddle.linalg.det(x)
A = paddle.linalg.det(x) >>> print(A)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
print(A) [-1.29280925, 0.77832544, 0.89754158])
# [ 0.02547996, 2.52317095, -6.15900707])
""" """
...@@ -1854,15 +1916,17 @@ def slogdet(x, name=None): ...@@ -1854,15 +1916,17 @@ def slogdet(x, name=None):
""" """
Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant. Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.
The determinant can be computed with ``sign * exp`` (logabsdet) The determinant can be computed with ``sign * exp`` (logabsdet).
Supports input of float, double Supports input of float, double.
Note that for matrices that have zero determinant, this returns ``(0, -inf)`` Note that for matrices that have zero determinant, this returns ``(0, -inf)``.
Args: Args:
x (Tensor): the batch of matrices of size :math:`(*, n, n)` x (Tensor): the batch of matrices of size :math:`(*, n, n)`
where math:`*` is one or more batch dimensions. where math:`*` is one or more batch dimensions.
name (str, optional): Name of the output.It's used to print debug info for
developers. Details: :ref:`api_guide_Name`. Default is None.
Returns: Returns:
y (Tensor), A tensor containing the sign of the determinant and the natural logarithm y (Tensor), A tensor containing the sign of the determinant and the natural logarithm
...@@ -1871,16 +1935,16 @@ def slogdet(x, name=None): ...@@ -1871,16 +1935,16 @@ def slogdet(x, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
x = paddle.randn([3,3,3]) >>> x = paddle.randn([3,3,3])
>>> A = paddle.linalg.slogdet(x)
A = paddle.linalg.slogdet(x) >>> print(A)
>>> # doctest: +SKIP
print(A) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-1. , 1. , 1. ],
# [[ 1. , 1. , -1. ], [ 0.25681755, -0.25061053, -0.10809582]])
# [-0.98610914, -0.43010661, -0.10872950]]) >>> # doctest: -SKIP
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -1931,8 +1995,8 @@ def svd(x, full_matrices=False, name=None): ...@@ -1931,8 +1995,8 @@ def svd(x, full_matrices=False, name=None):
If full_matrices = False, svd op will use a economic method to store U and V. If full_matrices = False, svd op will use a economic method to store U and V.
which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N). which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).
Default value is False. Default value is False.
name (str, optional): Name for the operation (optional, default is None). name (str, optional): Name for the operation. For more information,
For more information, please refer to :ref:`api_guide_Name`. please refer to :ref:`api_guide_Name`. Default value is None.
Returns: Returns:
- U (Tensor), is the singular value decomposition result U. - U (Tensor), is the singular value decomposition result U.
...@@ -1944,25 +2008,29 @@ def svd(x, full_matrices=False, name=None): ...@@ -1944,25 +2008,29 @@ def svd(x, full_matrices=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64') >>> x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')
x = x.reshape([3, 2]) >>> x = x.reshape([3, 2])
u, s, vh = paddle.linalg.svd(x) >>> u, s, vh = paddle.linalg.svd(x)
print (u) >>> print (u)
#U = [[ 0.27364809, -0.21695147 ], Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [ 0.37892198, -0.87112408 ], [[-0.27364809, -0.21695147],
# [ 0.8840446 , 0.44053933 ]] [-0.37892198, -0.87112408],
[-0.88404460, 0.44053933]])
print (s)
#S = [8.14753743, 0.78589688] >>> print (s)
print (vh) Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
#VT= [[ 0.51411221, 0.85772294], [8.14753743, 0.78589688])
# [ 0.85772294, -0.51411221]]
>>> print (vh)
# one can verify : U * S * VT == X Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# U * UH == I [[-0.51411221, -0.85772294],
# V * VH == I [ 0.85772294, -0.51411221]])
>>> # one can verify : U * S * VT == X
>>> # U * UH == I
>>> # V * VH == I
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -2002,8 +2070,9 @@ def pca_lowrank(x, q=None, center=True, niter=2, name=None): ...@@ -2002,8 +2070,9 @@ def pca_lowrank(x, q=None, center=True, niter=2, name=None):
Default value is :math:`q=min(6,N,M)`. Default value is :math:`q=min(6,N,M)`.
center (bool, optional): if True, center the input tensor. center (bool, optional): if True, center the input tensor.
Default value is True. Default value is True.
name (str, optional): Name for the operation (optional, default is None). niter (int, optional): number of iterations to perform. Default: 2.
For more information, please refer to :ref:`api_guide_Name`. name (str, optional): Name for the operation. For more information,
please refer to :ref:`api_guide_Name`. Default: None.
Returns: Returns:
- Tensor U, is N x q matrix. - Tensor U, is N x q matrix.
...@@ -2015,29 +2084,30 @@ def pca_lowrank(x, q=None, center=True, niter=2, name=None): ...@@ -2015,29 +2084,30 @@ def pca_lowrank(x, q=None, center=True, niter=2, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
x = paddle.randn((5, 5), dtype='float64')
U, S, V = paddle.linalg.pca_lowrank(x) >>> x = paddle.randn((5, 5), dtype='float64')
print(U) >>> U, S, V = paddle.linalg.pca_lowrank(x)
# Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True, >>> print(U)
# [[ 0.41057070, 0.40364287, 0.59099574, -0.34529432, 0.44721360], Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
# [-0.30243321, 0.55670611, -0.15025419, 0.61321785, 0.44721360], [[ 0.80131563, 0.11962647, 0.27667179, -0.25891214, 0.44721360],
# [ 0.57427340, -0.15936327, -0.66414981, -0.06097905, 0.44721360], [-0.12642301, 0.69917551, -0.17899393, 0.51296394, 0.44721360],
# [-0.63897516, -0.09968973, -0.17298615, -0.59316819, 0.44721360], [ 0.08997135, -0.69821706, -0.20059228, 0.51396579, 0.44721360],
# [-0.04343573, -0.70129598, 0.39639442, 0.38622370, 0.44721360]]) [-0.23871837, -0.02815453, -0.59888153, -0.61932365, 0.44721360],
[-0.52614559, -0.09243040, 0.70179595, -0.14869394, 0.44721360]])
print(S)
# Tensor(shape=[5], dtype=float64, place=Place(gpu:0), stop_gradient=True, >>> print(S)
# [3.33724265, 2.57573259, 1.69479048, 0.68069312, 0.00000000]) Tensor(shape=[5], dtype=float64, place=Place(cpu), stop_gradient=True,
[2.60101614, 2.40554940, 1.49768346, 0.19064830, 0.00000000])
print(V)
# Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True, >>> print(V)
# [[ 0.09800724, -0.32627008, -0.23593953, 0.81840445, 0.39810690], Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
# [-0.60100303, 0.63741176, -0.01953663, 0.09023999, 0.47326173], [[ 0.58339481, -0.17143771, 0.00522143, 0.57976310, 0.54231640],
# [ 0.25073864, -0.21305240, -0.32662950, -0.54786156, 0.69634740], [ 0.22334335, 0.72963474, -0.30148399, -0.39388750, 0.41438019],
# [ 0.33057205, 0.48282641, -0.75998527, 0.06744040, -0.27472705], [ 0.05416913, 0.34666487, 0.93549758, 0.00063507, 0.04162998],
# [ 0.67604895, 0.45688227, 0.50959437, 0.13179682, 0.23908071]]) [-0.39519094, 0.53074980, -0.16687419, 0.71175586, -0.16638919],
[-0.67131070, -0.19071018, 0.07795789, -0.04615811, 0.71046714]])
""" """
def conjugate(x): def conjugate(x):
...@@ -2172,25 +2242,28 @@ def matrix_power(x, n, name=None): ...@@ -2172,25 +2242,28 @@ def matrix_power(x, n, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1, 2, 3], >>> x = paddle.to_tensor([[1, 2, 3],
[1, 4, 9], ... [1, 4, 9],
[1, 8, 27]], dtype='float64') ... [1, 8, 27]], dtype='float64')
print(paddle.linalg.matrix_power(x, 2)) >>> print(paddle.linalg.matrix_power(x, 2))
# [[6. , 34. , 102.], Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
# [14. , 90. , 282.], [[6. , 34. , 102.],
# [36. , 250., 804.]] [14. , 90. , 282.],
[36. , 250., 804.]])
print(paddle.linalg.matrix_power(x, 0))
# [[1., 0., 0.], >>> print(paddle.linalg.matrix_power(x, 0))
# [0., 1., 0.], Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
# [0., 0., 1.]] [[1., 0., 0.],
[0., 1., 0.],
print(paddle.linalg.matrix_power(x, -2)) [0., 0., 1.]])
# [[ 12.91666667, -12.75000000, 2.83333333 ],
# [-7.66666667 , 8. , -1.83333333 ], >>> print(paddle.linalg.matrix_power(x, -2))
# [ 1.80555556 , -1.91666667 , 0.44444444 ]] Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 12.91666667, -12.75000000, 2.83333333 ],
[-7.66666667 , 8. , -1.83333333 ],
[ 1.80555556 , -1.91666667 , 0.44444444 ]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.matrix_power(x, n) return _C_ops.matrix_power(x, n)
...@@ -2218,14 +2291,14 @@ def qr(x, mode="reduced", name=None): ...@@ -2218,14 +2291,14 @@ def qr(x, mode="reduced", name=None):
x (Tensor): The input tensor. Its shape should be `[..., M, N]`, x (Tensor): The input tensor. Its shape should be `[..., M, N]`,
where ... is zero or more batch dimensions. M and N can be arbitrary where ... is zero or more batch dimensions. M and N can be arbitrary
positive number. The data type of x should be float32 or float64. positive number. The data type of x should be float32 or float64.
mode (str, optional): A flag to control the behavior of qr, the default is "reduced". mode (str, optional): A flag to control the behavior of qr.
Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`: Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:
If mode = "reduced", qr op will return reduced Q and R matrices, If mode = "reduced", qr op will return reduced Q and R matrices,
which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`. which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.
If mode = "complete", qr op will return complete Q and R matrices, If mode = "complete", qr op will return complete Q and R matrices,
which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`. which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.
If mode = "r", qr op will only return reduced R matrix, which means If mode = "r", qr op will only return reduced R matrix, which means
R's shape is `[..., K, N]`. R's shape is `[..., K, N]`. Default: "reduced".
name (str, optional): Name for the operation (optional, default is None). name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`.
...@@ -2236,21 +2309,21 @@ def qr(x, mode="reduced", name=None): ...@@ -2236,21 +2309,21 @@ def qr(x, mode="reduced", name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64') >>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')
q, r = paddle.linalg.qr(x) >>> q, r = paddle.linalg.qr(x)
print (q) >>> print (q)
print (r) Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-0.16903085, 0.89708523],
# Q = [[-0.16903085, 0.89708523], [-0.50709255, 0.27602622],
# [-0.50709255, 0.27602622], [-0.84515425, -0.34503278]])
# [-0.84515425, -0.34503278]]) >>> print (r)
Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# R = [[-5.91607978, -7.43735744], [[-5.91607978, -7.43735744],
# [ 0. , 0.82807867]]) [ 0. , 0.82807867]])
# one can verify : X = Q * R ; >>> # one can verify : X = Q * R ;
""" """
if in_dynamic_mode(): if in_dynamic_mode():
q, r = _C_ops.qr(x, mode) q, r = _C_ops.qr(x, mode)
...@@ -2318,42 +2391,41 @@ def lu(x, pivot=True, get_infos=False, name=None): ...@@ -2318,42 +2391,41 @@ def lu(x, pivot=True, get_infos=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64') >>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')
lu,p,info = paddle.linalg.lu(x, get_infos=True) >>> lu,p,info = paddle.linalg.lu(x, get_infos=True)
# >>> lu: >>> print(lu)
# Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[5. , 6. ], [[5. , 6. ],
# [0.20000000, 0.80000000], [0.20000000, 0.80000000],
# [0.60000000, 0.50000000]]) [0.60000000, 0.50000000]])
# >>> p >>> print(p)
# Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[2], dtype=int32, place=Place(cpu), stop_gradient=True,
# [3, 3]) [3, 3])
# >>> info >>> print(info)
# Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True,
# 0) [0])
P,L,U = paddle.linalg.lu_unpack(lu,p) >>> P,L,U = paddle.linalg.lu_unpack(lu,p)
# >>> P >>> print(P)
# (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[0., 1., 0.], [[0., 1., 0.],
# [0., 0., 1.], [0., 0., 1.],
# [1., 0., 0.]]), [1., 0., 0.]])
# >>> L >>> print(L)
# Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[1. , 0. ], [[1. , 0. ],
# [0.20000000, 1. ], [0.20000000, 1. ],
# [0.60000000, 0.50000000]]), [0.60000000, 0.50000000]])
# >>> U >>> print(U)
# Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[5. , 6. ], [[5. , 6. ],
# [0. , 0.80000000]])) [0. , 0.80000000]])
>>> # one can verify : X = P @ L @ U ;
# one can verify : X = P @ L @ U ;
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -2397,7 +2469,7 @@ def lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None): ...@@ -2397,7 +2469,7 @@ def lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):
y (Tensor): Pivots get from paddle.linalg.lu. y (Tensor): Pivots get from paddle.linalg.lu.
unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True. unpack_ludata (bool, optional): whether to unpack L and U from x. Default: True.
unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True. unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.
...@@ -2415,41 +2487,41 @@ def lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None): ...@@ -2415,41 +2487,41 @@ def lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64') >>> x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')
lu,p,info = paddle.linalg.lu(x, get_infos=True) >>> lu,p,info = paddle.linalg.lu(x, get_infos=True)
# >>> lu: >>> print(lu)
# Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[5. , 6. ], [[5. , 6. ],
# [0.20000000, 0.80000000], [0.20000000, 0.80000000],
# [0.60000000, 0.50000000]]) [0.60000000, 0.50000000]])
# >>> p >>> print(p)
# Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[2], dtype=int32, place=Place(cpu), stop_gradient=True,
# [3, 3]) [3, 3])
# >>> info >>> print(info)
# Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True,
# 0) [0])
P,L,U = paddle.linalg.lu_unpack(lu,p) >>> P,L,U = paddle.linalg.lu_unpack(lu,p)
# >>> P >>> print(P)
# (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[0., 1., 0.], [[0., 1., 0.],
# [0., 0., 1.], [0., 0., 1.],
# [1., 0., 0.]]), [1., 0., 0.]])
# >>> L >>> print(L)
# Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[3, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[1. , 0. ], [[1. , 0. ],
# [0.20000000, 1. ], [0.20000000, 1. ],
# [0.60000000, 0.50000000]]), [0.60000000, 0.50000000]])
# >>> U >>> print(U)
# Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True, Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[5. , 6. ], [[5. , 6. ],
# [0. , 0.80000000]])) [0. , 0.80000000]])
# one can verify : X = P @ L @ U ; >>> # one can verify : X = P @ L @ U ;
""" """
if x.ndim < 2: if x.ndim < 2:
raise ValueError( raise ValueError(
...@@ -2507,27 +2579,25 @@ def eig(x, name=None): ...@@ -2507,27 +2579,25 @@ def eig(x, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
paddle.device.set_device("cpu") >>> x = paddle.to_tensor([[1.6707249, 7.2249975, 6.5045543],
... [9.956216, 8.749598, 6.066444 ],
x = paddle.to_tensor([[1.6707249, 7.2249975, 6.5045543], ... [4.4251957, 1.7983172, 0.370647 ]])
[9.956216, 8.749598, 6.066444 ], >>> w, v = paddle.linalg.eig(x)
[4.4251957, 1.7983172, 0.370647 ]]) >>> print(v)
w, v = paddle.linalg.eig(x) Tensor(shape=[3, 3], dtype=complex64, place=Place(cpu), stop_gradient=True,
print(v) [[ (0.5061365365982056+0j) , (0.7971761226654053+0j) ,
# Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False, (0.1851806491613388+0j) ],
# [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) , [ (0.8308236598968506+0j) , (-0.3463813066482544+0j) ,
# (0.18518077798279986+0j)], (-0.6837005615234375+0j) ],
# [(-0.8308237755993192+0j) , (0.3463813401919749+0j) , [ (0.23142573237419128+0j), (-0.49449989199638367+0j),
# (-0.6837005269141947+0j) ], (0.7058765292167664+0j) ]])
# [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,
# (0.7058765252952796+0j) ]]) >>> print(w)
Tensor(shape=[3], dtype=complex64, place=Place(cpu), stop_gradient=True,
print(w) [ (16.50470733642578+0j) , (-5.503481388092041+0j) ,
# Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False, (-0.21026138961315155+0j)])
# [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,
# (-0.21026087843552282+0j)])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -2570,18 +2640,20 @@ def eigvals(x, name=None): ...@@ -2570,18 +2640,20 @@ def eigvals(x, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
paddle.set_device("cpu") >>> x = paddle.rand(shape=[3, 3], dtype='float64')
paddle.seed(1234) >>> print(x)
Tensor(shape=[3, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0.86583615, 0.52014721, 0.25960938],
[0.90525323, 0.42400090, 0.40641288],
[0.97020893, 0.74437359, 0.51785128]])
x = paddle.rand(shape=[3, 3], dtype='float64') >>> print(paddle.linalg.eigvals(x))
# [[0.02773777, 0.93004224, 0.06911496], Tensor(shape=[3], dtype=complex128, place=Place(cpu), stop_gradient=True,
# [0.24831591, 0.45733623, 0.07717843], [ (1.788956694280852+0j) , (0.16364484879581526+0j),
# [0.48016702, 0.14235102, 0.42620817]]) (-0.14491322408727625+0j)])
print(paddle.linalg.eigvals(x))
# [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128
""" """
x_shape = list(x.shape) x_shape = list(x.shape)
...@@ -2641,33 +2713,32 @@ def multi_dot(x, name=None): ...@@ -2641,33 +2713,32 @@ def multi_dot(x, name=None):
Args: Args:
x ([Tensor]): The input tensors which is a list Tensor. x ([Tensor]): The input tensors which is a list Tensor.
name(str|None): A name for this layer(optional). If set None, the layer name (str, optional): Name for the operation (optional, default is None).
will be named automatically. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
Tensor: The output Tensor. Tensor: The output Tensor.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
# A * B >>> # A * B
A = paddle.rand([3, 4]) >>> A = paddle.rand([3, 4])
B = paddle.rand([4, 5]) >>> B = paddle.rand([4, 5])
out = paddle.linalg.multi_dot([A, B]) >>> out = paddle.linalg.multi_dot([A, B])
print(out.shape) >>> print(out.shape)
# [3, 5] [3, 5]
# A * B * C >>> # A * B * C
A = paddle.rand([10, 5]) >>> A = paddle.rand([10, 5])
B = paddle.rand([5, 8]) >>> B = paddle.rand([5, 8])
C = paddle.rand([8, 7]) >>> C = paddle.rand([8, 7])
out = paddle.linalg.multi_dot([A, B, C]) >>> out = paddle.linalg.multi_dot([A, B, C])
print(out.shape) >>> print(out.shape)
# [10, 7] [10, 7]
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -2703,9 +2774,9 @@ def eigh(x, UPLO='L', name=None): ...@@ -2703,9 +2774,9 @@ def eigh(x, UPLO='L', name=None):
Args: Args:
x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x
should be one of float32, float64, complex64, complex128. should be one of float32, float64, complex64, complex128.
UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix, UPLO (str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,
"'U' represents the upper triangular matrix.". "'U' represents the upper triangular matrix.". Default: 'L'.
name(str, optional): The default value is None. Normally there is no need for user to set this 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`. property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -2717,15 +2788,17 @@ def eigh(x, UPLO='L', name=None): ...@@ -2717,15 +2788,17 @@ def eigh(x, UPLO='L', name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1, -2j], [2j, 5]]) >>> x = paddle.to_tensor([[1, -2j], [2j, 5]])
out_value, out_vector = paddle.linalg.eigh(x, UPLO='L') >>> out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')
print(out_value) >>> print(out_value)
#[0.17157288, 5.82842712] Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
print(out_vector) [0.17157286, 5.82842731])
#[(-0.9238795325112867+0j), (-0.3826834323650898+0j)], >>> print(out_vector)
#[ 0.3826834323650898j , -0.9238795325112867j ]] Tensor(shape=[2, 2], dtype=complex64, place=Place(cpu), stop_gradient=True,
[[(-0.9238795042037964+0j), (-0.3826833963394165+0j)],
[ 0.3826833963394165j , -0.9238795042037964j ]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -2789,21 +2862,18 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None): ...@@ -2789,21 +2862,18 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
If x is hermitian or symmetric matrix, svd will be replaced with eigh. If x is hermitian or symmetric matrix, svd will be replaced with eigh.
Args: Args:
x(Tensor): The input tensor. Its shape should be (*, m, n) x (Tensor): The input tensor. Its shape should be (*, m, n)
where * is zero or more batch dimensions. m and n can be where * is zero or more batch dimensions. m and n can be
arbitraty positive number. The data type of x should be arbitraty positive number. The data type of x should be
float32 or float64 or complex64 or complex128. When data float32 or float64 or complex64 or complex128. When data
type is complex64 or cpmplex128, hermitian should be set type is complex64 or cpmplex128, hermitian should be set
True. True.
rcond (Tensor, optional): the tolerance value to determine
rcond(Tensor, optional): the tolerance value to determine
when is a singular value zero. Default:1e-15. when is a singular value zero. Default:1e-15.
hermitian (bool, optional): indicates whether x is Hermitian
hermitian(bool, optional): indicates whether x is Hermitian
if complex or symmetric if real. Default: False. if complex or symmetric if real. Default: False.
name (str, optional): The default value is None. Normally there is no need for user to set this
name(str|None): A name for this layer(optional). If set None, property. For more information, please refer to :ref:`api_guide_Name`.
the layer will be named automatically.
Returns: Returns:
Tensor: The tensor with same data type with x. it represents Tensor: The tensor with same data type with x. it represents
...@@ -2812,25 +2882,24 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None): ...@@ -2812,25 +2882,24 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.arange(15).reshape((3, 5)).astype('float64') >>> x = paddle.arange(15).reshape((3, 5)).astype('float64')
input = paddle.to_tensor(x) >>> input = paddle.to_tensor(x)
out = paddle.linalg.pinv(input) >>> out = paddle.linalg.pinv(input)
print(input) >>> print(input)
print(out) Tensor(shape=[3, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[0. , 1. , 2. , 3. , 4. ],
# input: [5. , 6. , 7. , 8. , 9. ],
# [[0. , 1. , 2. , 3. , 4. ], [10., 11., 12., 13., 14.]])
# [5. , 6. , 7. , 8. , 9. ],
# [10., 11., 12., 13., 14.]] >>> print(out)
Tensor(shape=[5, 3], dtype=float64, place=Place(cpu), stop_gradient=True,
# out: [[-0.22666667, -0.06666667, 0.09333333],
# [[-0.22666667, -0.06666667, 0.09333333], [-0.12333333, -0.03333333, 0.05666667],
# [-0.12333333, -0.03333333, 0.05666667], [-0.02000000, -0.00000000, 0.02000000],
# [-0.02000000, 0.00000000, 0.02000000], [ 0.08333333, 0.03333333, -0.01666667],
# [ 0.08333333, 0.03333333, -0.01666667], [ 0.18666667, 0.06666667, -0.05333333]])
# [ 0.18666667, 0.06666667, -0.05333333]]
# one can verify : x * out * x = x ; # one can verify : x * out * x = x ;
# or out * x * out = x ; # or out * x * out = x ;
...@@ -3034,7 +3103,7 @@ def solve(x, y, name=None): ...@@ -3034,7 +3103,7 @@ def solve(x, y, name=None):
more batch dimensions. Its data type should be float32 or float64. more batch dimensions. Its data type should be float32 or float64.
y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be ``[*, M, K]``, where ``*`` is zero or y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be ``[*, M, K]``, where ``*`` is zero or
more batch dimensions. Its data type should be float32 or float64. more batch dimensions. Its data type should be float32 or float64.
name(str, optional): Name for the operation (optional, default is None). name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -3045,18 +3114,19 @@ def solve(x, y, name=None): ...@@ -3045,18 +3114,19 @@ def solve(x, y, name=None):
.. code-block:: python .. code-block:: python
# a square system of linear equations: >>> # a square system of linear equations:
# 2*X0 + X1 = 9 >>> # 2*X0 + X1 = 9
# X0 + 2*X1 = 8 >>> # X0 + 2*X1 = 8
import paddle >>> import paddle
x = paddle.to_tensor([[3, 1],[1, 2]], dtype="float64") >>> x = paddle.to_tensor([[3, 1],[1, 2]], dtype="float64")
y = paddle.to_tensor([9, 8], dtype="float64") >>> y = paddle.to_tensor([9, 8], dtype="float64")
out = paddle.linalg.solve(x, y) >>> out = paddle.linalg.solve(x, y)
print(out) >>> print(out)
# [2., 3.]) Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
[2., 3.])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.solve(x, y) return _C_ops.solve(x, y)
...@@ -3077,7 +3147,7 @@ def triangular_solve( ...@@ -3077,7 +3147,7 @@ def triangular_solve(
x, y, upper=True, transpose=False, unitriangular=False, name=None x, y, upper=True, transpose=False, unitriangular=False, name=None
): ):
r""" r"""
Computes the solution of a system of equations with a triangular coefficient. `x` is coefficient matrix Computes the solution of a system of equations with a triangular coefficient. `x` is coefficient matrix
`y` is multiple right-hand sides of equations. `y` is multiple right-hand sides of equations.
Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs is also Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs is also
...@@ -3103,7 +3173,7 @@ def triangular_solve( ...@@ -3103,7 +3173,7 @@ def triangular_solve(
transpose (bool, optional): whether `x` should be transposed before calculation. Default: False. transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.
unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed
to be 1 and not referenced from `x` . Default: False. to be 1 and not referenced from `x` . Default: False.
name(str, optional): Name for the operation (optional, default is None). name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -3112,20 +3182,23 @@ def triangular_solve( ...@@ -3112,20 +3182,23 @@ def triangular_solve(
Examples: Examples:
.. code-block:: python .. code-block:: python
# a square system of linear equations: >>> # a square system of linear equations:
# x1 + x2 + x3 = 0 >>> # x1 + x2 + x3 = 0
# 2*x2 + x3 = -9 >>> # 2*x2 + x3 = -9
# -x3 = 5 >>> # -x3 = 5
import paddle >>> import paddle
x = paddle.to_tensor([[1, 1, 1], >>> x = paddle.to_tensor([[1, 1, 1],
[0, 2, 1], ... [0, 2, 1],
[0, 0,-1]], dtype="float64") ... [0, 0,-1]], dtype="float64")
y = paddle.to_tensor([[0], [-9], [5]], dtype="float64") >>> y = paddle.to_tensor([[0], [-9], [5]], dtype="float64")
out = paddle.linalg.triangular_solve(x, y, upper=True) >>> out = paddle.linalg.triangular_solve(x, y, upper=True)
print(out) >>> print(out)
# [7, -2, -5] Tensor(shape=[3, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 7.],
[-2.],
[-5.]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.triangular_solve(x, y, upper, transpose, unitriangular) return _C_ops.triangular_solve(x, y, upper, transpose, unitriangular)
...@@ -3166,7 +3239,7 @@ def cholesky_solve(x, y, upper=False, name=None): ...@@ -3166,7 +3239,7 @@ def cholesky_solve(x, y, upper=False, name=None):
y (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or y (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or
more batch dimensions. Its data type should be float32 or float64. more batch dimensions. Its data type should be float32 or float64.
upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False. upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.
name(str, optional): Name for the operation (optional, default is None). name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -3175,16 +3248,19 @@ def cholesky_solve(x, y, upper=False, name=None): ...@@ -3175,16 +3248,19 @@ def cholesky_solve(x, y, upper=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
u = paddle.to_tensor([[1, 1, 1], >>> u = paddle.to_tensor([[1, 1, 1],
[0, 2, 1], ... [0, 2, 1],
[0, 0,-1]], dtype="float64") ... [0, 0,-1]], dtype="float64")
b = paddle.to_tensor([[0], [-9], [5]], dtype="float64") >>> b = paddle.to_tensor([[0], [-9], [5]], dtype="float64")
out = paddle.linalg.cholesky_solve(b, u, upper=True) >>> out = paddle.linalg.cholesky_solve(b, u, upper=True)
print(out) >>> print(out)
# [-2.5, -7, 9.5] Tensor(shape=[3, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
[[-2.50000000],
[-7. ],
[ 9.50000000]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.cholesky_solve(x, y, upper) return _C_ops.cholesky_solve(x, y, upper)
...@@ -3225,13 +3301,13 @@ def eigvalsh(x, UPLO='L', name=None): ...@@ -3225,13 +3301,13 @@ def eigvalsh(x, UPLO='L', name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[1, -2j], [2j, 5]]) >>> x = paddle.to_tensor([[1, -2j], [2j, 5]])
out_value = paddle.eigvalsh(x, UPLO='L') >>> out_value = paddle.eigvalsh(x, UPLO='L')
print(out_value) >>> print(out_value)
# Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
# [0.17157286, 5.82842731]) [0.17157286, 5.82842731])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
values, _ = _C_ops.eigvalsh(x, UPLO, x.stop_gradient) values, _ = _C_ops.eigvalsh(x, UPLO, x.stop_gradient)
...@@ -3312,31 +3388,36 @@ def lstsq(x, y, rcond=None, driver=None, name=None): ...@@ -3312,31 +3388,36 @@ def lstsq(x, y, rcond=None, driver=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
paddle.set_device("cpu") >>> x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])
x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]]) >>> y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])
y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]]) >>> results = paddle.linalg.lstsq(x, y, driver="gelsd")
results = paddle.linalg.lstsq(x, y, driver="gelsd") >>> print(results[0])
print(results[0]) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[ 0.78350395, -0.22165027, -0.62371236], [[ 0.78350395, -0.22165027, -0.62371236],
# [-0.11340097, 0.78866047, 1.14948535]] [-0.11340097, 0.78866047, 1.14948535]])
print(results[1]) >>> print(results[1])
# [19.81443405, 10.43814468, 30.56185532]) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
print(results[2]) [19.81443405, 10.43814468, 30.56185532])
# 2 >>> print(results[2])
print(results[3]) Tensor(shape=[], dtype=int32, place=Place(cpu), stop_gradient=True,
# [9.03455734, 1.54167950] 2)
>>> print(results[3])
x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]]) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]]) [9.03455734, 1.54167950])
results = paddle.linalg.lstsq(x, y, driver="gels")
print(results[0]) >>> x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])
# [[ 0.39386186, 0.10230173, 0.93606132], >>> y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])
# [ 0.10741687, -0.29028133, 0.11892585], >>> results = paddle.linalg.lstsq(x, y, driver="gels")
# [-0.05115091, 0.51918161, -0.19948854]] >>> print(results[0])
print(results[1]) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [] [[ 0.39386186, 0.10230169, 0.93606132],
[ 0.10741688, -0.29028130, 0.11892584],
[-0.05115093, 0.51918161, -0.19948851]])
>>> print(results[1])
Tensor(shape=[0], dtype=float32, place=Place(cpu), stop_gradient=True,
[])
""" """
device = paddle.get_device() device = paddle.get_device()
if device == "cpu": if device == "cpu":
...@@ -3456,11 +3537,11 @@ def corrcoef(x, rowvar=True, name=None): ...@@ -3456,11 +3537,11 @@ def corrcoef(x, rowvar=True, name=None):
The values of `R` are between -1 and 1. The values of `R` are between -1 and 1.
Parameters: Args:
x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below. x (Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.
rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True. rowvar (bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True.
name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`. name (str, optional): Name of the output. It's used to print debug info for developers. Details: :ref:`api_guide_Name`. Default: None.
Returns: Returns:
...@@ -3469,15 +3550,15 @@ def corrcoef(x, rowvar=True, name=None): ...@@ -3469,15 +3550,15 @@ def corrcoef(x, rowvar=True, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
xt = paddle.rand((3,4))
print(paddle.linalg.corrcoef(xt))
# Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, >>> xt = paddle.rand((3,4))
# [[ 1. , -0.73702252, 0.66228950], >>> print(paddle.linalg.corrcoef(xt))
# [-0.73702258, 1. , -0.77104872], Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [ 0.66228974, -0.77104825, 1. ]]) [[ 0.99999988, -0.47689581, -0.89559376],
[-0.47689593, 1. , 0.16345492],
[-0.89559382, 0.16345496, 1. ]])
""" """
if len(x.shape) > 2 or len(x.shape) < 1: if len(x.shape) > 2 or len(x.shape) < 1:
...@@ -3545,13 +3626,15 @@ def cdist( ...@@ -3545,13 +3626,15 @@ def cdist(
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([[0.9041, 0.0196], [-0.3108, -2.4423], [-0.4821, 1.059]], dtype=paddle.float32) >>> x = paddle.to_tensor([[0.9041, 0.0196], [-0.3108, -2.4423], [-0.4821, 1.059]], dtype=paddle.float32)
y = paddle.to_tensor([[-2.1763, -0.4713], [-0.6986, 1.3702]], dtype=paddle.float32) >>> y = paddle.to_tensor([[-2.1763, -0.4713], [-0.6986, 1.3702]], dtype=paddle.float32)
distance = paddle.cdist(x, y) >>> distance = paddle.cdist(x, y)
print(distance) >>> print(distance)
# Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[3.1193, 2.0959], [2.7138, 3.8322], [2.2830, 0.3791]]) [[3.11927032, 2.09589314],
[2.71384072, 3.83217239],
[2.28300953, 0.37910119]])
""" """
check_variable_and_dtype(x, 'x', ('float32', 'float64'), 'cdist') check_variable_and_dtype(x, 'x', ('float32', 'float64'), 'cdist')
......
python/paddle/tensor/logic.py
浏览文件 @ 4dbe441c
...@@ -129,12 +129,15 @@ def logical_and(x, y, out=None, name=None): ...@@ -129,12 +129,15 @@ def logical_and(x, y, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> x = paddle.to_tensor([True])
>>> y = paddle.to_tensor([True, False, True, False])
>>> res = paddle.logical_and(x, y)
>>> print(res)
Tensor(shape=[4], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False, True , False])
x = paddle.to_tensor([True])
y = paddle.to_tensor([True, False, True, False])
res = paddle.logical_and(x, y)
print(res) # [True False True False]
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.logical_and(x, y) return _C_ops.logical_and(x, y)
...@@ -188,15 +191,15 @@ def logical_or(x, y, out=None, name=None): ...@@ -188,15 +191,15 @@ def logical_or(x, y, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1]) >>> x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1])
y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2]) >>> y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2])
res = paddle.logical_or(x, y) >>> res = paddle.logical_or(x, y)
print(res) >>> print(res)
# Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True, Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True,
# [[True , True ], [[True , True ],
# [True , False]]) [True , False]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.logical_or(x, y) return _C_ops.logical_or(x, y)
...@@ -249,15 +252,15 @@ def logical_xor(x, y, out=None, name=None): ...@@ -249,15 +252,15 @@ def logical_xor(x, y, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1]) >>> x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1])
y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2]) >>> y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2])
res = paddle.logical_xor(x, y) >>> res = paddle.logical_xor(x, y)
print(res) >>> print(res)
# Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True, Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True,
# [[False, True ], [[False, True ],
# [True , False]]) [True , False]])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.logical_xor(x, y) return _C_ops.logical_xor(x, y)
...@@ -300,6 +303,7 @@ def logical_not(x, out=None, name=None): ...@@ -300,6 +303,7 @@ def logical_not(x, out=None, name=None):
.. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
Args: Args:
x(Tensor): Operand of logical_not operator. Must be a Tensor of type bool, int8, int16, in32, in64, float16, float32, or float64, complex64, complex128. x(Tensor): Operand of logical_not operator. Must be a Tensor of type bool, int8, int16, in32, in64, float16, float32, or float64, complex64, complex128.
out(Tensor): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor` will be created to save the output. out(Tensor): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor` will be created to save the output.
name(str|None): The default value is None. Normally there is no need for users to set this property. For more information, please refer to :ref:`api_guide_Name`. name(str|None): The default value is None. Normally there is no need for users to set this property. For more information, please refer to :ref:`api_guide_Name`.
...@@ -310,11 +314,13 @@ def logical_not(x, out=None, name=None): ...@@ -310,11 +314,13 @@ def logical_not(x, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([True, False, True, False]) >>> x = paddle.to_tensor([True, False, True, False])
res = paddle.logical_not(x) >>> res = paddle.logical_not(x)
print(res) # [False True False True] >>> print(res)
Tensor(shape=[4], dtype=bool, place=Place(cpu), stop_gradient=True,
[False, True , False, True ])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.logical_not(x) return _C_ops.logical_not(x)
...@@ -340,9 +346,7 @@ def is_empty(x, name=None): ...@@ -340,9 +346,7 @@ def is_empty(x, name=None):
Args: Args:
x (Tensor): The Tensor to be tested. x (Tensor): The Tensor to be tested.
name (str, optional): The default value is ``None`` . Normally users name (str, optional): The default value is ``None`` . Normally users don't have to set this parameter. For more information, please refer to :ref:`api_guide_Name` .
don't have to set this parameter. For more information,
please refer to :ref:`api_guide_Name` .
Returns: Returns:
Tensor: A bool scalar Tensor. True if 'x' is an empty Tensor. Tensor: A bool scalar Tensor. True if 'x' is an empty Tensor.
...@@ -350,12 +354,13 @@ def is_empty(x, name=None): ...@@ -350,12 +354,13 @@ def is_empty(x, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
input = paddle.rand(shape=[4, 32, 32], dtype='float32') >>> input = paddle.rand(shape=[4, 32, 32], dtype='float32')
res = paddle.is_empty(x=input) >>> res = paddle.is_empty(x=input)
# res: Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, >>> print(res)
# False) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
False)
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -394,15 +399,19 @@ def equal_all(x, y, name=None): ...@@ -394,15 +399,19 @@ def equal_all(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 2, 3]) >>> y = paddle.to_tensor([1, 2, 3])
z = paddle.to_tensor([1, 4, 3]) >>> z = paddle.to_tensor([1, 4, 3])
result1 = paddle.equal_all(x, y) >>> result1 = paddle.equal_all(x, y)
print(result1) # result1 = True >>> print(result1)
result2 = paddle.equal_all(x, z) Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
print(result2) # result2 = False True)
>>> result2 = paddle.equal_all(x, z)
>>> print(result2)
Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
False)
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.equal_all(x, y) return _C_ops.equal_all(x, y)
...@@ -429,11 +438,11 @@ def allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None): ...@@ -429,11 +438,11 @@ def allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None):
two tensors are elementwise equal within a tolerance. two tensors are elementwise equal within a tolerance.
Args: Args:
x(Tensor): The input tensor, it's data type should be float16, float32, float64.. x (Tensor): The input tensor, it's data type should be float16, float32, float64.
y(Tensor): The input tensor, it's data type should be float16, float32, float64.. y (Tensor): The input tensor, it's data type should be float16, float32, float64.
rtol(rtoltype, optional): The relative tolerance. Default: :math:`1e-5` . rtol (rtoltype, optional): The relative tolerance. Default: :math:`1e-5` .
atol(atoltype, optional): The absolute tolerance. Default: :math:`1e-8` . atol (atoltype, optional): The absolute tolerance. Default: :math:`1e-8` .
equal_nan(equalnantype, optional): ${equal_nan_comment}. equal_nan (equalnantype, optional): ${equal_nan_comment}. Default: False.
name (str, optional): Name for the operation. For more information, please name (str, optional): Name for the operation. For more information, please
refer to :ref:`api_guide_Name`. Default: None. refer to :ref:`api_guide_Name`. Default: None.
...@@ -443,27 +452,28 @@ def allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None): ...@@ -443,27 +452,28 @@ def allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([10000., 1e-07]) >>> x = paddle.to_tensor([10000., 1e-07])
y = paddle.to_tensor([10000.1, 1e-08]) >>> y = paddle.to_tensor([10000.1, 1e-08])
result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, >>> result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan")
equal_nan=False, name="ignore_nan") >>> print(result1)
# False Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
False)
result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, >>> result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan")
equal_nan=True, name="equal_nan") >>> print(result2)
# False Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
False)
x = paddle.to_tensor([1.0, float('nan')]) >>> x = paddle.to_tensor([1.0, float('nan')])
y = paddle.to_tensor([1.0, float('nan')]) >>> y = paddle.to_tensor([1.0, float('nan')])
result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, >>> result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan")
equal_nan=False, name="ignore_nan") >>> print(result1)
# False Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
False)
result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, >>> result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan")
equal_nan=True, name="equal_nan") >>> print(result2)
# True Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True,
True)
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -502,9 +512,9 @@ def equal(x, y, name=None): ...@@ -502,9 +512,9 @@ def equal(x, y, name=None):
The output has no gradient. The output has no gradient.
Args: Args:
x(Tensor): Tensor, data type is bool, float16, float32, float64, int32, int64. x (Tensor): Tensor, data type is bool, float16, float32, float64, int32, int64.
y(Tensor): Tensor, data type is bool, float16, float32, float64, int32, int64. y (Tensor): Tensor, data type is bool, float16, float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no need for 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`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -514,12 +524,14 @@ def equal(x, y, name=None): ...@@ -514,12 +524,14 @@ def equal(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 2]) >>> y = paddle.to_tensor([1, 3, 2])
result1 = paddle.equal(x, y) >>> result1 = paddle.equal(x, y)
print(result1) # result1 = [True False False] >>> print(result1)
Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False, False])
""" """
if not isinstance(y, (int, bool, float, Variable)): if not isinstance(y, (int, bool, float, Variable)):
raise TypeError( raise TypeError(
...@@ -599,9 +611,9 @@ def greater_equal(x, y, name=None): ...@@ -599,9 +611,9 @@ def greater_equal(x, y, name=None):
The output has no gradient. The output has no gradient.
Args: Args:
x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no need for 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`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Tensor: The output shape is same as input :attr:`x`. The output data type is bool.
...@@ -609,12 +621,14 @@ def greater_equal(x, y, name=None): ...@@ -609,12 +621,14 @@ def greater_equal(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 2]) >>> y = paddle.to_tensor([1, 3, 2])
result1 = paddle.greater_equal(x, y) >>> result1 = paddle.greater_equal(x, y)
print(result1) # result1 = [True False True] >>> print(result1)
Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False, True ])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.greater_equal(x, y) return _C_ops.greater_equal(x, y)
...@@ -685,9 +699,9 @@ def greater_than(x, y, name=None): ...@@ -685,9 +699,9 @@ def greater_than(x, y, name=None):
The output has no gradient. The output has no gradient.
Args: Args:
x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no need for 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`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
Tensor: The output shape is same as input :attr:`x`. The output data type is bool. Tensor: The output shape is same as input :attr:`x`. The output data type is bool.
...@@ -695,12 +709,14 @@ def greater_than(x, y, name=None): ...@@ -695,12 +709,14 @@ def greater_than(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 2]) >>> y = paddle.to_tensor([1, 3, 2])
result1 = paddle.greater_than(x, y) >>> result1 = paddle.greater_than(x, y)
print(result1) # result1 = [False False True] >>> print(result1)
Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
[False, False, True ])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.greater_than(x, y) return _C_ops.greater_than(x, y)
...@@ -771,9 +787,9 @@ def less_equal(x, y, name=None): ...@@ -771,9 +787,9 @@ def less_equal(x, y, name=None):
The output has no gradient. The output has no gradient.
Args: Args:
x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no need for 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`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -782,12 +798,14 @@ def less_equal(x, y, name=None): ...@@ -782,12 +798,14 @@ def less_equal(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 2]) >>> y = paddle.to_tensor([1, 3, 2])
result1 = paddle.less_equal(x, y) >>> result1 = paddle.less_equal(x, y)
print(result1) # result1 = [True True False] >>> print(result1)
Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , True , False])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.less_equal(x, y) return _C_ops.less_equal(x, y)
...@@ -858,9 +876,9 @@ def less_than(x, y, name=None): ...@@ -858,9 +876,9 @@ def less_than(x, y, name=None):
The output has no gradient. The output has no gradient.
Args: Args:
x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64. y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float16, float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no need for 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`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -869,12 +887,14 @@ def less_than(x, y, name=None): ...@@ -869,12 +887,14 @@ def less_than(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 2]) >>> y = paddle.to_tensor([1, 3, 2])
result1 = paddle.less_than(x, y) >>> result1 = paddle.less_than(x, y)
print(result1) # result1 = [False True False] >>> print(result1)
Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
[False, True , False])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.less_than(x, y) return _C_ops.less_than(x, y)
...@@ -945,9 +965,9 @@ def not_equal(x, y, name=None): ...@@ -945,9 +965,9 @@ def not_equal(x, y, name=None):
The output has no gradient. The output has no gradient.
Args: Args:
x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64. x (Tensor): First input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64. y (Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
name(str, optional): The default value is None. Normally there is no need for 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`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -956,12 +976,14 @@ def not_equal(x, y, name=None): ...@@ -956,12 +976,14 @@ def not_equal(x, y, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([1, 2, 3]) >>> x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 2]) >>> y = paddle.to_tensor([1, 3, 2])
result1 = paddle.not_equal(x, y) >>> result1 = paddle.not_equal(x, y)
print(result1) # result1 = [False True True] >>> print(result1)
Tensor(shape=[3], dtype=bool, place=Place(cpu), stop_gradient=True,
[False, True , True ])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
return _C_ops.not_equal(x, y) return _C_ops.not_equal(x, y)
...@@ -1037,15 +1059,17 @@ def is_tensor(x): ...@@ -1037,15 +1059,17 @@ def is_tensor(x):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
input1 = paddle.rand(shape=[2, 3, 5], dtype='float32') >>> input1 = paddle.rand(shape=[2, 3, 5], dtype='float32')
check = paddle.is_tensor(input1) >>> check = paddle.is_tensor(input1)
print(check) #True >>> print(check)
True
input3 = [1, 4] >>> input3 = [1, 4]
check = paddle.is_tensor(input3) >>> check = paddle.is_tensor(input3)
print(check) #False >>> print(check)
False
""" """
if in_dynamic_mode(): if in_dynamic_mode():
...@@ -1113,7 +1137,9 @@ def bitwise_and(x, y, out=None, name=None): ...@@ -1113,7 +1137,9 @@ def bitwise_and(x, y, out=None, name=None):
Args: Args:
x (Tensor): Input Tensor of ``bitwise_and`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. x (Tensor): Input Tensor of ``bitwise_and`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
y (Tensor): Input Tensor of ``bitwise_and`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. y (Tensor): Input Tensor of ``bitwise_and`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
out(Tensor): Result of ``bitwise_and`` . It is a N-D Tensor with the same data type of input Tensor. out (Tensor, optional): Result of ``bitwise_and`` . It is a N-D Tensor with the same data type of input Tensor. Default: None.
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`.
Returns: Returns:
Tensor: Result of ``bitwise_and`` . It is a N-D Tensor with the same data type of input Tensor. Tensor: Result of ``bitwise_and`` . It is a N-D Tensor with the same data type of input Tensor.
...@@ -1121,11 +1147,13 @@ def bitwise_and(x, y, out=None, name=None): ...@@ -1121,11 +1147,13 @@ def bitwise_and(x, y, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([-5, -1, 1]) >>> x = paddle.to_tensor([-5, -1, 1])
y = paddle.to_tensor([4, 2, -3]) >>> y = paddle.to_tensor([4, 2, -3])
res = paddle.bitwise_and(x, y) >>> res = paddle.bitwise_and(x, y)
print(res) # [0, 2, 1] >>> print(res)
Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 2, 1])
""" """
if in_dynamic_mode() and out is None: if in_dynamic_mode() and out is None:
return _C_ops.bitwise_and(x, y) return _C_ops.bitwise_and(x, y)
...@@ -1167,7 +1195,9 @@ def bitwise_or(x, y, out=None, name=None): ...@@ -1167,7 +1195,9 @@ def bitwise_or(x, y, out=None, name=None):
Args: Args:
x (Tensor): Input Tensor of ``bitwise_or`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. x (Tensor): Input Tensor of ``bitwise_or`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
y (Tensor): Input Tensor of ``bitwise_or`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. y (Tensor): Input Tensor of ``bitwise_or`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
out(Tensor): Result of ``bitwise_or`` . It is a N-D Tensor with the same data type of input Tensor. out (Tensor, optional): Result of ``bitwise_or`` . It is a N-D Tensor with the same data type of input Tensor. Default: None.
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`.
Returns: Returns:
Tensor: Result of ``bitwise_or`` . It is a N-D Tensor with the same data type of input Tensor. Tensor: Result of ``bitwise_or`` . It is a N-D Tensor with the same data type of input Tensor.
...@@ -1175,11 +1205,13 @@ def bitwise_or(x, y, out=None, name=None): ...@@ -1175,11 +1205,13 @@ def bitwise_or(x, y, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([-5, -1, 1]) >>> x = paddle.to_tensor([-5, -1, 1])
y = paddle.to_tensor([4, 2, -3]) >>> y = paddle.to_tensor([4, 2, -3])
res = paddle.bitwise_or(x, y) >>> res = paddle.bitwise_or(x, y)
print(res) # [-1, -1, -3] >>> print(res)
Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
[-1, -1, -3])
""" """
if in_dynamic_mode() and out is None: if in_dynamic_mode() and out is None:
return _C_ops.bitwise_or(x, y) return _C_ops.bitwise_or(x, y)
...@@ -1222,7 +1254,9 @@ def bitwise_xor(x, y, out=None, name=None): ...@@ -1222,7 +1254,9 @@ def bitwise_xor(x, y, out=None, name=None):
Args: Args:
x (Tensor): Input Tensor of ``bitwise_xor`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. x (Tensor): Input Tensor of ``bitwise_xor`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
y (Tensor): Input Tensor of ``bitwise_xor`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. y (Tensor): Input Tensor of ``bitwise_xor`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
out(Tensor): Result of ``bitwise_xor`` . It is a N-D Tensor with the same data type of input Tensor. out (Tensor, optional): Result of ``bitwise_xor`` . It is a N-D Tensor with the same data type of input Tensor. Default: None.
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`.
Returns: Returns:
Tensor: Result of ``bitwise_xor`` . It is a N-D Tensor with the same data type of input Tensor. Tensor: Result of ``bitwise_xor`` . It is a N-D Tensor with the same data type of input Tensor.
...@@ -1230,11 +1264,13 @@ def bitwise_xor(x, y, out=None, name=None): ...@@ -1230,11 +1264,13 @@ def bitwise_xor(x, y, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([-5, -1, 1]) >>> x = paddle.to_tensor([-5, -1, 1])
y = paddle.to_tensor([4, 2, -3]) >>> y = paddle.to_tensor([4, 2, -3])
res = paddle.bitwise_xor(x, y) >>> res = paddle.bitwise_xor(x, y)
print(res) # [-1, -3, -4] >>> print(res)
Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
[-1, -3, -4])
""" """
if in_dynamic_mode() and out is None: if in_dynamic_mode() and out is None:
return _C_ops.bitwise_xor(x, y) return _C_ops.bitwise_xor(x, y)
...@@ -1275,7 +1311,9 @@ def bitwise_not(x, out=None, name=None): ...@@ -1275,7 +1311,9 @@ def bitwise_not(x, out=None, name=None):
Args: Args:
x (Tensor): Input Tensor of ``bitwise_not`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64. x (Tensor): Input Tensor of ``bitwise_not`` . It is a N-D Tensor of bool, uint8, int8, int16, int32, int64.
out(Tensor): Result of ``bitwise_not`` . It is a N-D Tensor with the same data type of input Tensor. out (Tensor, optional): Result of ``bitwise_not`` . It is a N-D Tensor with the same data type of input Tensor. Default: None.
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`.
Returns: Returns:
Tensor: Result of ``bitwise_not`` . It is a N-D Tensor with the same data type of input Tensor. Tensor: Result of ``bitwise_not`` . It is a N-D Tensor with the same data type of input Tensor.
...@@ -1283,10 +1321,12 @@ def bitwise_not(x, out=None, name=None): ...@@ -1283,10 +1321,12 @@ def bitwise_not(x, out=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([-5, -1, 1]) >>> x = paddle.to_tensor([-5, -1, 1])
res = paddle.bitwise_not(x) >>> res = paddle.bitwise_not(x)
print(res) # [4, 0, -2] >>> print(res)
Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
[ 4, 0, -2])
""" """
if in_dynamic_mode() and out is None: if in_dynamic_mode() and out is None:
return _C_ops.bitwise_not(x) return _C_ops.bitwise_not(x)
...@@ -1334,25 +1374,32 @@ def isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None): ...@@ -1334,25 +1374,32 @@ def isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.to_tensor([10000., 1e-07]) >>> x = paddle.to_tensor([10000., 1e-07])
y = paddle.to_tensor([10000.1, 1e-08]) >>> y = paddle.to_tensor([10000.1, 1e-08])
result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, >>> result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
equal_nan=False, name="ignore_nan") ... equal_nan=False, name="ignore_nan")
# [True, False] >>> print(result1)
result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
equal_nan=True, name="equal_nan") [True , False])
# [True, False] >>> result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
... equal_nan=True, name="equal_nan")
x = paddle.to_tensor([1.0, float('nan')]) >>> print(result2)
y = paddle.to_tensor([1.0, float('nan')]) Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, [True , False])
equal_nan=False, name="ignore_nan") >>> x = paddle.to_tensor([1.0, float('nan')])
# [True, False] >>> y = paddle.to_tensor([1.0, float('nan')])
result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, >>> result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
equal_nan=True, name="equal_nan") ... equal_nan=False, name="ignore_nan")
# [True, True] >>> print(result1)
Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
[True , False])
>>> result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
... equal_nan=True, name="equal_nan")
>>> print(result2)
Tensor(shape=[2], dtype=bool, place=Place(cpu), stop_gradient=True,
[True, True])
""" """
if in_dynamic_mode(): if in_dynamic_mode():
......
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