Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
P
Paddle
未验证 提交 861fef52 编写于 作者: W wanghuancoder 提交者： GitHub
浏览文件
操作

delete legacy dygraph code in python/paddle/tensor (#49286) 

* delete _in_legacy_dygraph
上级 ea741aff
显示空白变更内容
内联 并排
Showing with 4091 addition and 5104 deletion
python/paddle/fluid/framework.py
浏览文件 @ 861fef52
......@@ -255,8 +255,7 @@ def _test_eager_guard(place=None):
try:
yield
finally:
if not already_fallback:
_enable_legacy_dygraph()
pass
global_ipu_index = -1
......
python/paddle/fluid/tests/unittests/test_unique.py
浏览文件 @ 861fef52
......@@ -28,7 +28,9 @@ class TestUniqueOp(OpTest):
self.init_config()
def test_check_output(self):
paddle.enable_static()
self.check_output()
paddle.disable_static()
def init_config(self):
self.inputs = {
......@@ -72,6 +74,8 @@ class TestRandom(TestUniqueOp):
class TestUniqueRaiseError(unittest.TestCase):
def test_errors(self):
paddle.enable_static()
def test_type():
paddle.unique([10])
......@@ -82,6 +86,7 @@ class TestUniqueRaiseError(unittest.TestCase):
paddle.unique(data)
self.assertRaises(TypeError, test_dtype)
paddle.disable_static()
@unittest.skipIf(
......@@ -100,8 +105,10 @@ class TestOneGPU(TestUniqueOp):
def test_check_output(self):
if core.is_compiled_with_cuda():
paddle.enable_static()
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=1e-5)
paddle.disable_static()
@unittest.skipIf(
......@@ -125,8 +132,10 @@ class TestRandomGPU(TestUniqueOp):
def test_check_output(self):
if core.is_compiled_with_cuda():
paddle.enable_static()
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=1e-5)
paddle.disable_static()
class TestSortedUniqueOp(TestUniqueOp):
......@@ -209,16 +218,13 @@ class TestUniqueOpAxis1(TestUniqueOp):
class TestUniqueAPI(unittest.TestCase):
def test_dygraph_api_out(self):
paddle.disable_static()
x_data = x_data = np.random.randint(0, 10, (120))
x = paddle.to_tensor(x_data)
out = paddle.unique(x)
expected_out = np.unique(x_data)
self.assertTrue((out.numpy() == expected_out).all(), True)
paddle.enable_static()
def test_dygraph_api_attr(self):
paddle.disable_static()
x_data = np.random.random((3, 5, 5)).astype("float32")
x = paddle.to_tensor(x_data)
out, index, inverse, counts = paddle.unique(
......@@ -239,10 +245,8 @@ class TestUniqueAPI(unittest.TestCase):
self.assertTrue((index.numpy() == np_index).all(), True)
self.assertTrue((inverse.numpy() == np_inverse).all(), True)
self.assertTrue((counts.numpy() == np_counts).all(), True)
paddle.enable_static()
def test_dygraph_attr_dtype(self):
paddle.disable_static()
x_data = x_data = np.random.randint(0, 10, (120))
x = paddle.to_tensor(x_data)
out, indices, inverse, counts = paddle.unique(
......@@ -259,9 +263,9 @@ class TestUniqueAPI(unittest.TestCase):
self.assertTrue((indices.numpy() == np_indices).all(), True)
self.assertTrue((inverse.numpy() == np_inverse).all(), True)
self.assertTrue((counts.numpy() == np_counts).all(), True)
paddle.enable_static()
def test_static_graph(self):
paddle.enable_static()
with paddle.static.program_guard(
paddle.static.Program(), paddle.static.Program()
):
......@@ -281,6 +285,7 @@ class TestUniqueAPI(unittest.TestCase):
np.testing.assert_allclose(result[0], np_unique, rtol=1e-05)
np.testing.assert_allclose(result[1], np_inverse, rtol=1e-05)
np.testing.assert_allclose(result[2], np_counts, rtol=1e-05)
paddle.disable_static()
class TestUniqueError(unittest.TestCase):
......@@ -295,6 +300,7 @@ class TestUniqueError(unittest.TestCase):
self.assertRaises(TypeError, test_x_dtype)
def test_attr(self):
paddle.enable_static()
x = paddle.fluid.data(name='x', shape=[10, 10], dtype='float64')
def test_return_index():
......@@ -319,6 +325,7 @@ class TestUniqueError(unittest.TestCase):
result = paddle.unique(x, dtype='float64')
self.assertRaises(TypeError, test_axis)
paddle.disable_static()
if __name__ == "__main__":
......
python/paddle/tensor/array.py
浏览文件 @ 861fef52
......@@ -15,7 +15,7 @@
# Define functions about array.
from ..fluid.data_feeder import check_type, check_variable_and_dtype
from ..framework import LayerHelper, _non_static_mode, core
from ..framework import LayerHelper, core, in_dygraph_mode
from ..static import Variable
__all__ = []
......@@ -45,12 +45,12 @@ def array_length(array):
arr_len = paddle.tensor.array_length(arr)
print(arr_len) # 1
"""
if _non_static_mode():
if in_dygraph_mode():
assert isinstance(
array, list
), "The 'array' in array_write must be a list in dygraph mode"
return len(array)
else:
if (
not isinstance(array, Variable)
or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY
......@@ -63,7 +63,9 @@ def array_length(array):
tmp = helper.create_variable_for_type_inference(dtype='int64')
tmp.stop_gradient = True
helper.append_op(
type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]}
type='lod_array_length',
inputs={'X': [array]},
outputs={'Out': [tmp]},
)
return tmp
......@@ -107,7 +109,7 @@ def array_read(array, i):
item = paddle.tensor.array_read(arr, i)
print(item) # [[5., 5., 5.]]
"""
if _non_static_mode():
if in_dygraph_mode():
assert isinstance(
array, list
), "The 'array' in array_read must be list in dygraph mode"
......@@ -119,7 +121,7 @@ def array_read(array, i):
], "The shape of index 'i' should be [1] in dygraph mode"
i = i.numpy().item(0)
return array[i]
else:
check_variable_and_dtype(i, 'i', ['int64'], 'array_read')
helper = LayerHelper('array_read', **locals())
if (
......@@ -167,7 +169,7 @@ def array_write(x, i, array=None):
item = paddle.tensor.array_read(arr, i)
print(item) # [[5., 5., 5.]]
"""
if _non_static_mode():
if in_dygraph_mode():
assert isinstance(
x, Variable
), "The input data 'x' in array_write must be Variable in dygraph mode"
......@@ -191,7 +193,7 @@ def array_write(x, i, array=None):
else:
array.append(x)
return array
else:
check_variable_and_dtype(i, 'i', ['int64'], 'array_write')
check_type(x, 'x', (Variable), 'array_write')
helper = LayerHelper('array_write', **locals())
......@@ -265,9 +267,9 @@ def create_array(dtype, initialized_list=None):
)
)
if _non_static_mode():
if in_dygraph_mode():
return array
else:
helper = LayerHelper("array", **locals())
tensor_array = helper.create_variable(
name="{0}.out".format(helper.name),
......
python/paddle/tensor/attribute.py
浏览文件 @ 861fef52
......@@ -17,10 +17,10 @@
import numpy as np
import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle import _C_ops
from ..fluid.data_feeder import check_type, check_variable_and_dtype
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode
from ..fluid.framework import in_dygraph_mode
from ..framework import LayerHelper, core
from ..static import Variable
from .creation import _complex_to_real_dtype, assign
......@@ -107,11 +107,7 @@ def shape(input):
out = _C_ops.shape(input)
out.stop_gradient = True
return out
if _in_legacy_dygraph():
out = _legacy_C_ops.shape(input)
out.stop_gradient = True
return out
else:
check_variable_and_dtype(
input,
'input',
......@@ -289,9 +285,7 @@ def real(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.real(x)
if _in_legacy_dygraph():
return _legacy_C_ops.real(x)
else:
check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'real')
helper = LayerHelper('real', **locals())
out = helper.create_variable_for_type_inference(
......@@ -336,9 +330,7 @@ def imag(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.imag(x)
if _in_legacy_dygraph():
return _legacy_C_ops.imag(x)
else:
check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'imag')
helper = LayerHelper('imag', **locals())
out = helper.create_variable_for_type_inference(
......
python/paddle/tensor/creation.py
浏览文件 @ 861fef52
......@@ -33,7 +33,6 @@ from ..fluid.data_feeder import (
from ..fluid.framework import (
Variable,
_in_eager_without_dygraph_check,
_in_legacy_dygraph,
device_guard,
)
from ..fluid.initializer import Constant, Initializer
......@@ -43,7 +42,6 @@ from ..framework import (
LayerHelper,
_current_expected_place,
_get_paddle_place,
_non_static_mode,
convert_np_dtype_to_dtype_,
core,
in_dygraph_mode,
......@@ -324,11 +322,7 @@ def linspace(start, stop, num, dtype=None, name=None):
dtype,
_current_expected_place(),
)
if _in_legacy_dygraph():
return _legacy_C_ops.linspace(
tensor_start, tensor_stop, tensor_num, 'dtype', dtype
)
else:
helper = LayerHelper("linspace", **locals())
start_dtype = convert_dtype(tensor_start.dtype)
......@@ -376,7 +370,11 @@ def linspace(start, stop, num, dtype=None, name=None):
helper.append_op(
type='linspace',
inputs={'Start': tensor_start, 'Stop': tensor_stop, 'Num': tensor_num},
inputs={
'Start': tensor_start,
'Stop': tensor_stop,
'Num': tensor_num,
},
attrs={'dtype': dtype},
outputs={'Out': [out]},
)
......@@ -446,11 +444,11 @@ def logspace(start, stop, num, base=10.0, dtype=None, name=None):
if not isinstance(base, Variable):
with device_guard("cpu"):
tensor_base = fill_constant([1], dtype, base)
if _non_static_mode():
if in_dygraph_mode():
return _legacy_C_ops.logspace(
tensor_start, tensor_stop, tensor_num, tensor_base, 'dtype', dtype
)
else:
helper = LayerHelper("logspace", **locals())
start_dtype = convert_dtype(tensor_start.dtype)
......@@ -746,7 +744,7 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
if place is None:
place = _current_expected_place()
if _non_static_mode():
if paddle.fluid.framework._non_static_mode():
return _to_tensor_non_static(data, dtype, place, stop_gradient)
# call assign for static graph
......@@ -785,32 +783,41 @@ def full_like(x, fill_value, dtype=None, name=None):
# [[2. 2. 2.]
# [2. 2. 2.]]
"""
if dtype is None:
dtype = x.dtype
else:
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
return _C_ops.full_like(x, fill_value, dtype, x.place)
if _in_legacy_dygraph():
return _legacy_C_ops.fill_any_like(
x, 'value', fill_value, 'dtype', dtype
)
else:
helper = LayerHelper("full_like", **locals())
check_variable_and_dtype(
x,
'x',
['bool', 'float16', 'float32', 'float64', 'int16', 'int32', 'int64'],
[
'bool',
'float16',
'float32',
'float64',
'int16',
'int32',
'int64',
],
'full_like',
)
check_dtype(
dtype,
'dtype',
['bool', 'float16', 'float32', 'float64', 'int16', 'int32', 'int64'],
[
'bool',
'float16',
'float32',
'float64',
'int16',
'int32',
'int64',
],
'full_like/zeros_like/ones_like',
)
out = helper.create_variable_for_type_inference(dtype=dtype)
......@@ -1011,7 +1018,7 @@ def eye(num_rows, num_columns=None, dtype=None, name=None):
"""
def _check_attr(attr, message):
if isinstance(attr, ((Variable, core.VarBase, core.eager.Tensor))):
if isinstance(attr, ((Variable, core.eager.Tensor))):
assert len(attr.shape) == 1 and attr.shape[0] in [1, -1]
elif not isinstance(attr, int) or attr < 0:
raise TypeError("{} should be a non-negative int.".format(message))
......@@ -1027,16 +1034,10 @@ def eye(num_rows, num_columns=None, dtype=None, name=None):
else:
num_columns = num_rows
if _non_static_mode():
if in_dygraph_mode():
out = _C_ops.eye(
num_rows, num_columns, dtype, _current_expected_place()
)
elif _in_legacy_dygraph():
out = _legacy_C_ops.eye(
'dtype', dtype, 'num_rows', num_rows, 'num_columns', num_columns
)
else:
helper = LayerHelper("eye", **locals())
check_dtype(
......@@ -1211,14 +1212,12 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
if in_dygraph_mode():
return _C_ops.arange(start, end, step, dtype, _current_expected_place())
if _in_legacy_dygraph():
out = _legacy_C_ops.range(start, end, step)
out.stop_gradient = True
return out
else:
check_dtype(
dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'], 'range/arange'
dtype,
'dtype',
['float32', 'float64', 'int32', 'int64'],
'range/arange',
)
helper = LayerHelper('range', **locals())
out = helper.create_variable_for_type_inference(dtype, shape=out_shape)
......@@ -1328,11 +1327,7 @@ def tril(x, diagonal=0, name=None):
"""
if in_dygraph_mode():
return _C_ops.tril(x, diagonal, True)
if _in_legacy_dygraph():
op = getattr(_legacy_C_ops, 'tril_triu')
return op(x, 'diagonal', diagonal, "lower", True)
else:
return _tril_triu_op(LayerHelper('tril', **locals()))
......@@ -1394,11 +1389,7 @@ def triu(x, diagonal=0, name=None):
"""
if in_dygraph_mode():
return _C_ops.triu(x, diagonal, False)
if _in_legacy_dygraph():
op = getattr(_legacy_C_ops, 'tril_triu')
return op(x, 'diagonal', diagonal, "lower", False)
else:
return _tril_triu_op(LayerHelper('triu', **locals()))
......@@ -1437,18 +1428,16 @@ def meshgrid(*args, **kwargs):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
args = args[0]
if _in_legacy_dygraph():
num = len(args)
out = _legacy_C_ops.meshgrid(list(args), num)
return out
if in_dygraph_mode():
return _C_ops.meshgrid(list(args))
else:
name = kwargs.get("name", None)
helper = LayerHelper('meshgrid', **locals())
if not isinstance(args, (list, tuple)):
raise TypeError("The type of input args in meshgrid should be list.")
raise TypeError(
"The type of input args in meshgrid should be list."
)
for id, input_ in enumerate(args):
check_dtype(
......@@ -1555,27 +1544,14 @@ def diagflat(x, offset=0, name=None):
# [0, 0, 3, 0, 0],
# [0, 0, 0, 4, 0]])
"""
padding_value = 0
if in_dygraph_mode():
if len(x.shape) <= 1:
return _C_ops.diag(x, offset, padding_value)
return _C_ops.diag(x, offset, 0)
else:
y = _C_ops.flatten(x, 0, -1)
return _C_ops.diag(y, offset, padding_value)
if _in_legacy_dygraph():
if len(x.shape) == 1:
return _legacy_C_ops.diag_v2(
x, "offset", offset, "padding_value", padding_value
)
return _C_ops.diag(y, offset, 0)
else:
y, _ = _legacy_C_ops.flatten_contiguous_range(
x, "start_axis", 0, "stop_axis", -1
)
return _legacy_C_ops.diag_v2(
y, "offset", offset, "padding_value", padding_value
)
padding_value = 0
check_type(x, 'x', (Variable), 'diagflat')
check_dtype(
x.dtype, 'x', ['float32', 'float64', 'int32', 'int64'], 'diagflat'
......@@ -1690,11 +1666,6 @@ def diag(x, offset=0, padding_value=0, name=None):
"""
if in_dygraph_mode():
return _C_ops.diag(x, offset, padding_value)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.diag_v2(
x, "offset", offset, "padding_value", padding_value
)
else:
check_type(x, 'x', (Variable), 'diag_v2')
check_dtype(
......@@ -1782,15 +1753,7 @@ def empty(shape, dtype=None, name=None):
)
out.stop_gradient = True
return out
if _in_legacy_dygraph():
shape = utils.convert_shape_to_list(shape)
out = _legacy_C_ops.empty(
'shape', shape, 'dtype', convert_np_dtype_to_dtype_(dtype)
)
out.stop_gradient = True
return out
else:
helper = LayerHelper("empty", **locals())
inputs = {}
......@@ -1863,14 +1826,7 @@ def empty_like(x, dtype=None, name=None):
)
out.stop_gradient = True
return out
if _in_legacy_dygraph():
out = _legacy_C_ops.empty(
'shape', x.shape, 'dtype', convert_np_dtype_to_dtype_(dtype)
)
out.stop_gradient = True
return out
else:
helper = LayerHelper("empty_like", **locals())
check_variable_and_dtype(
x,
......@@ -1958,10 +1914,6 @@ def assign(x, output=None):
output = _C_ops.assign(input)
else:
_C_ops.assign_out_(input, output)
elif _in_legacy_dygraph():
if output is None:
output = core.VarBase()
_legacy_C_ops.assign(input, output)
else:
check_dtype(
input.dtype,
......@@ -2060,18 +2012,6 @@ def assign(x, output=None):
values,
_current_expected_place(),
)
elif _in_legacy_dygraph():
if output is None:
output = core.VarBase()
_legacy_C_ops.assign_value(
output,
'shape',
list(input.shape),
'dtype',
dtype,
value_name,
values,
)
else:
if output is None:
output = helper.create_variable_for_type_inference(
......@@ -2087,9 +2027,6 @@ def assign(x, output=None):
},
)
if is_inplace and _in_legacy_dygraph():
output._bump_inplace_version()
return output
......@@ -2227,12 +2164,13 @@ def complex(real, imag, name=None):
"""
if in_dygraph_mode():
return _C_ops.complex(real, imag)
if paddle.in_dynamic_mode():
return paddle._legacy_C_ops.complex(real, imag)
check_variable_and_dtype(real, 'real', ['float32', 'float64'], 'complex')
check_variable_and_dtype(imag, 'imag', ['float32', 'float64'], 'complex')
else:
check_variable_and_dtype(
real, 'real', ['float32', 'float64'], 'complex'
)
check_variable_and_dtype(
imag, 'imag', ['float32', 'float64'], 'complex'
)
op_type = "complex"
helper = LayerHelper(op_type, **locals())
......@@ -2242,7 +2180,9 @@ def complex(real, imag, name=None):
)
outputs = {"Out": out}
attrs = {}
helper.append_op(type=op_type, inputs=inputs, attrs=attrs, outputs=outputs)
helper.append_op(
type=op_type, inputs=inputs, attrs=attrs, outputs=outputs
)
return out
......@@ -2291,6 +2231,17 @@ def tril_indices(row, col, offset=0, dtype='int64'):
# [[ 1, 2, 2, 3, 3, 3],
# [ 0, 0, 1, 0, 1, 2]]
"""
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
if col is None:
col = row
out = _C_ops.tril_indices(
row, col, offset, dtype, _current_expected_place()
)
return out
else:
if not isinstance(row, int) or row < 0:
raise TypeError("row should be a non-negative int")
......@@ -2303,22 +2254,6 @@ def tril_indices(row, col, offset=0, dtype='int64'):
if not isinstance(offset, int):
raise TypeError("offset should be a int")
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
out = _C_ops.tril_indices(
row, col, offset, dtype, _current_expected_place()
)
return out
if _in_legacy_dygraph():
out = _legacy_C_ops.tril_indices(
'rows', row, 'cols', col, 'offset', offset, "dtype", dtype
)
return out
else:
helper = LayerHelper("tril_indices", **locals())
out = helper.create_variable_for_type_inference(dtype=dtype)
......@@ -2375,6 +2310,17 @@ def triu_indices(row, col=None, offset=0, dtype='int64'):
# [[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3],
# [0, 1, 2, 3, 0, 1, 2, 3, 1, 2, 3, 2, 3]]
"""
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
if col is None:
col = row
out = _C_ops.triu_indices(
row, col, offset, dtype, _current_expected_place()
)
return out
else:
if not isinstance(row, int) or row < 0:
raise TypeError("row should be a non-negative int")
......@@ -2387,22 +2333,6 @@ def triu_indices(row, col=None, offset=0, dtype='int64'):
if not isinstance(offset, int):
raise TypeError("offset should be a int")
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
out = _C_ops.triu_indices(
row, col, offset, dtype, _current_expected_place()
)
return out
if _in_legacy_dygraph():
out = _legacy_C_ops.triu_indices(
'row', row, 'col', col, 'offset', offset, "dtype", dtype
)
return out
else:
helper = LayerHelper("triu_indices", **locals())
out = helper.create_variable_for_type_inference(dtype=dtype)
......
python/paddle/tensor/einsum.py
浏览文件 @ 861fef52
......@@ -20,10 +20,10 @@ import string
import numpy as np
import opt_einsum
from paddle import _C_ops, _legacy_C_ops
from paddle import _C_ops
from ..fluid.data_feeder import check_type, check_variable_and_dtype
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode
from ..fluid.framework import in_dygraph_mode
from ..fluid.layer_helper import LayerHelper
from .linalg import matmul, transpose
from .manipulation import reshape, squeeze, unsqueeze
......@@ -829,18 +829,15 @@ def gen_einsum_op(equation, *operands):
"""
EinsumOp Python Interface:
"""
assert len(operands) <= 2, "Only support two operands in EinsumOp."
if in_dygraph_mode():
return _C_ops.einsum(operands, equation)[0]
if _in_legacy_dygraph():
# dygraph
return _legacy_C_ops.einsum(
operands, len(operands), len(operands), 'equation', equation
)[0]
else:
assert len(operands) <= 2, "Only support two operands in EinsumOp."
for inp in operands:
check_variable_and_dtype(inp, 'dtype', ['float32', 'float64'], 'einsum')
check_variable_and_dtype(
inp, 'dtype', ['float32', 'float64'], 'einsum'
)
check_type(equation, 'equation', str, 'einsum')
helper = LayerHelper('einsum', **locals())
out = helper.create_variable_for_type_inference(dtype=operands[0].dtype)
......
python/paddle/tensor/layer_function_generator.py
浏览文件 @ 861fef52
......@@ -24,7 +24,6 @@ from ..fluid.proto import framework_pb2
from ..framework import (
LayerHelper,
OpProtoHolder,
_non_static_mode,
convert_np_dtype_to_dtype_,
core,
in_dygraph_mode,
......@@ -274,15 +273,16 @@ def generate_activation_fn(op_type):
op_proto = OpProtoHolder.instance().get_op_proto(op_type)
def func(x, name=None):
if in_dygraph_mode() and hasattr(_C_ops, op_type):
if in_dygraph_mode():
if hasattr(_C_ops, op_type):
op = getattr(_C_ops, op_type)
return op(x)
else:
# TODO(dev): Because some ops' yaml has not been migrated.
# Replace it with _in_legacy_dygraph while all yaml work is done.
if _non_static_mode():
# Replace it with _C_ops while all yaml work is done.
op = getattr(_legacy_C_ops, op_type)
return op(x)
else:
if op_type not in ["abs", "exp", "square"]:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], op_type
......@@ -307,7 +307,9 @@ def generate_activation_fn(op_type):
helper = LayerHelper(op_type, **locals())
output = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": output})
helper.append_op(
type=op_type, inputs={"X": x}, outputs={"Out": output}
)
return output
func.__name__ = op_type
......@@ -332,12 +334,14 @@ def generate_inplace_fn(inplace_op_type):
origin_op_type = inplace_op_type[:-1]
def func(x, name=None):
if in_dygraph_mode() and hasattr(_C_ops, inplace_op_type):
if in_dygraph_mode():
if hasattr(_C_ops, inplace_op_type):
op = getattr(_C_ops, inplace_op_type)
return op(x)
if _non_static_mode():
else:
op = getattr(_legacy_C_ops, inplace_op_type)
return op(x)
else:
warnings.warn(
"In static mode, {}() is the same as {}() and does not perform inplace operation.".format(
inplace_op_type, origin_op_type
......
python/paddle/tensor/linalg.py
浏览文件 @ 861fef52
......@@ -15,7 +15,7 @@
import numpy as np
import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle import _C_ops
from paddle.common_ops_import import VarDesc
from ..fluid.data_feeder import (
......@@ -23,8 +23,7 @@ from ..fluid.data_feeder import (
check_type,
check_variable_and_dtype,
)
from ..fluid.framework import _in_legacy_dygraph
from ..framework import LayerHelper, _non_static_mode, in_dygraph_mode
from ..framework import LayerHelper, in_dygraph_mode
from ..static import Variable
from .creation import full
from .logic import logical_not
......@@ -90,10 +89,6 @@ def transpose(x, perm, name=None):
if in_dygraph_mode():
return _C_ops.transpose(x, perm)
else:
if _in_legacy_dygraph():
out, _ = _legacy_C_ops.transpose2(x, 'axis', perm)
return out
check_variable_and_dtype(
x,
'x',
......@@ -235,12 +230,7 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.matmul(x, y, transpose_x, transpose_y)
if _in_legacy_dygraph():
op_type = 'matmul_v2'
op = getattr(_legacy_C_ops, op_type)
return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)
else:
attrs = {
'trans_x': transpose_x,
'trans_y': transpose_y,
......@@ -252,7 +242,13 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
check_variable_and_dtype(
val,
name,
['float16', 'float32', 'float64', 'complex64', 'complex128'],
[
'float16',
'float32',
'float64',
'complex64',
'complex128',
],
'matmul',
)
......@@ -373,14 +369,7 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
if dim is None:
return _C_ops.frobenius_norm(input, [], keepdim, True)
return _C_ops.frobenius_norm(input, dim, keepdim, False)
if _in_legacy_dygraph():
if dim is None:
return _legacy_C_ops.frobenius_norm(
input, 'keep_dim', keepdim, 'reduce_all', True
)
return _legacy_C_ops.frobenius_norm(
input, 'dim', dim, 'keep_dim', keepdim, 'reduce_all', False
)
else:
attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}
if dim is None:
attrs['reduce_all'] = True
......@@ -416,22 +405,7 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
if axis is None:
axis = -1
return _C_ops.p_norm(input, porder, axis, 1e-12, keepdim, asvector)
if _in_legacy_dygraph():
if axis is None:
axis = -1
return _legacy_C_ops.p_norm(
input,
'porder',
porder,
'axis',
axis,
'keepdim',
keepdim,
'asvector',
asvector,
)
else:
if porder is not None:
check_type(porder, 'porder', (float, int), 'p_norm')
if axis is not None:
......@@ -469,17 +443,21 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
return _C_ops.max(out, axis, keepdim)
else:
return _C_ops.min(out, axis, keepdim)
else:
helper = LayerHelper('inf_norm', **locals())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})
helper.append_op(
type='abs', inputs={'X': input}, outputs={'Out': out}
)
reduce_out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
reduce_all = True if axis is None or axis == [] or asvector else False
reduce_all = (
True if axis is None or axis == [] or asvector else False
)
axis = axis if axis is not None and axis != [] else [0]
reduce_type = (
......@@ -489,7 +467,11 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
type=reduce_type,
inputs={'X': out},
outputs={'Out': reduce_out},
attrs={'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all},
attrs={
'dim': axis,
'keep_dim': keepdim,
'reduce_all': reduce_all,
},
)
return reduce_out
......@@ -846,40 +828,6 @@ def cond(x, p=None, name=None):
return _C_ops.max(sum_out, [-1], False)
if porder == -1 or porder == -np.inf:
return _C_ops.min(sum_out, [-1], False)
elif _in_legacy_dygraph():
reduce_all = True if axis is None or axis == [] else False
axis = axis if axis is not None and axis != [] else [0]
abs_out = _legacy_C_ops.abs(input)
sum_out = _legacy_C_ops.reduce_sum(
abs_out,
'dim',
axis,
'keepdim',
False,
'reduce_all',
reduce_all,
)
if porder == 1 or porder == np.inf:
return _legacy_C_ops.reduce_max(
sum_out,
'dim',
[-1],
'keepdim',
False,
'reduce_all',
reduce_all,
)
if porder == -1 or porder == -np.inf:
return _legacy_C_ops.reduce_min(
sum_out,
'dim',
[-1],
'keepdim',
False,
'reduce_all',
reduce_all,
)
else:
reduce_all = True if axis is None or axis == [] else False
axis = axis if axis is not None and axis != [] else [0]
......@@ -940,29 +888,7 @@ def cond(x, p=None, name=None):
sum_out_1 = _C_ops.sum(pow_out, axis, None, False)
sum_out_2 = _C_ops.sum(sum_out_1, axis, None, False)
return _C_ops.pow(sum_out_2, float(1.0 / porder))
elif paddle.in_dynamic_mode():
reduce_all = True if axis is None or axis == [] else False
pow_out = _legacy_C_ops.pow(input, 'factor', porder)
sum_out_1 = _legacy_C_ops.reduce_sum(
pow_out,
'dim',
axis,
'keepdim',
False,
'reduce_all',
reduce_all,
)
sum_out_2 = _legacy_C_ops.reduce_sum(
sum_out_1,
'dim',
axis,
'keepdim',
False,
'reduce_all',
reduce_all,
)
return _legacy_C_ops.pow(sum_out_2, 'factor', float(1.0 / porder))
else:
reduce_all = True if axis is None or axis == [] else False
block = LayerHelper('norm', **locals())
pow_out = block.create_variable_for_type_inference(
......@@ -987,13 +913,21 @@ def cond(x, p=None, name=None):
type='reduce_sum',
inputs={'X': pow_out},
outputs={'Out': sum_out_1},
attrs={'dim': axis, 'keep_dim': False, 'reduce_all': reduce_all},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
block.append_op(
type='reduce_sum',
inputs={'X': sum_out_1},
outputs={'Out': sum_out_2},
attrs={'dim': axis, 'keep_dim': False, 'reduce_all': reduce_all},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
block.append_op(
type='pow',
......@@ -1009,48 +943,19 @@ def cond(x, p=None, name=None):
Calculate the matrix norm, which is related to singular values, of a matrix
or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.
"""
if not in_dygraph_mode():
reduce_all = True if axis is None or axis == [] else False
u, s, vh = svd(input, full_matrices=False)
if _non_static_mode():
if porder == "nuc":
if in_dygraph_mode():
if porder == "nuc":
return _C_ops.sum(s, axis, None, False)
else:
return _legacy_C_ops.reduce_sum(
s,
'dim',
axis,
'keepdim',
False,
'reduce_all',
reduce_all,
)
if in_dygraph_mode():
max_out = _C_ops.max(s, axis, False)
min_out = _C_ops.min(s, axis, False)
if porder == 2:
return _C_ops.divide(max_out, min_out)
if porder == -2:
return _C_ops.divide(min_out, max_out)
else:
max_out = _legacy_C_ops.reduce_max(
s, 'dim', axis, 'keepdim', False, 'reduce_all', reduce_all
)
min_out = _legacy_C_ops.reduce_min(
s, 'dim', axis, 'keepdim', False, 'reduce_all', reduce_all
)
if porder == 2:
return _legacy_C_ops.elementwise_div(
max_out, min_out, 'aixs', axis, 'use_mkldnn', False
)
if porder == -2:
return _legacy_C_ops.elementwise_div(
min_out, max_out, 'aixs', axis, 'use_mkldnn', False
)
reduce_all = True if axis is None or axis == [] else False
block = LayerHelper('norm', **locals())
out = block.create_variable_for_type_inference(
dtype=block.input_dtype()
......@@ -1077,13 +982,21 @@ def cond(x, p=None, name=None):
type='reduce_max',
inputs={'X': s},
outputs={'Out': max_out},
attrs={'dim': axis, 'keep_dim': False, 'reduce_all': reduce_all},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
block.append_op(
type='reduce_min',
inputs={'X': s},
outputs={'Out': min_out},
attrs={'dim': axis, 'keep_dim': False, 'reduce_all': reduce_all},
attrs={
'dim': axis,
'keep_dim': False,
'reduce_all': reduce_all,
},
)
if porder == 2:
block.append_op(
......@@ -1103,7 +1016,7 @@ def cond(x, p=None, name=None):
return out
def empty_tensor(input, shape):
if paddle.in_dynamic_mode():
if in_dygraph_mode():
return input.reshape(shape)
raise ValueError("only support x is nonempty tensor in static mode")
......@@ -1186,9 +1099,7 @@ def dot(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.dot(x, y)
if _in_legacy_dygraph():
return _legacy_C_ops.dot(x, y)
else:
op_type = 'dot'
assert x is not None, 'x cannot be None in {}'.format(op_type)
......@@ -1389,15 +1300,7 @@ def t(input, name=None):
perm = [1, 0]
out = _C_ops.transpose(input, perm)
return out
if _in_legacy_dygraph():
if len(input.shape) == 1:
return input
# 2-D tensor
perm = [1, 0]
out, _ = _legacy_C_ops.transpose2(input, 'axis', perm)
return out
else:
check_variable_and_dtype(
input,
'input',
......@@ -1461,12 +1364,6 @@ def cross(x, y, axis=9, name=None):
if in_dygraph_mode():
axis = K_DEFAULT_DIM if axis is None else axis
return _C_ops.cross(x, y, axis)
else:
if _in_legacy_dygraph():
if axis is not None:
return _legacy_C_ops.cross(x, y, 'dim', axis)
else:
return _legacy_C_ops.cross(x, y)
else:
helper = LayerHelper("cross", **locals())
out = helper.create_variable_for_type_inference(x.dtype)
......@@ -1520,10 +1417,7 @@ def cholesky(x, upper=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.cholesky(x, upper)
if _in_legacy_dygraph():
return _legacy_C_ops.cholesky(x, "upper", upper)
else:
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')
check_type(upper, 'upper', bool, 'cholesky')
helper = LayerHelper('cholesky', **locals())
......@@ -1594,34 +1488,7 @@ def matrix_rank(x, tol=None, hermitian=False, name=None):
tol_attr = float(tol)
use_default_tol = False
return _C_ops.matrix_rank(x, tol_attr, hermitian, use_default_tol)
if _in_legacy_dygraph():
if tol is None:
tol_tensor = None
tol_attr = 0.0
use_default_tol = True
elif isinstance(tol, Variable):
if tol.dtype != x.dtype:
tol_tensor = cast(tol, x.dtype)
else:
tol_tensor = tol
tol_attr = 0.0
use_default_tol = False
else:
tol_tensor = None
tol_attr = float(tol)
use_default_tol = False
return _legacy_C_ops.matrix_rank(
x,
tol_tensor,
"tol",
tol_attr,
'hermitian',
hermitian,
'use_default_tol',
use_default_tol,
)
inputs = {}
attrs = {}
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')
......@@ -1711,13 +1578,12 @@ def bmm(x, y, name=None):
if in_dygraph_mode():
return _C_ops.bmm(x, y)
if paddle.in_dynamic_mode():
return _legacy_C_ops.bmm(x, y)
else:
helper = LayerHelper('bmm', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})
helper.append_op(
type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out}
)
return out
......@@ -1748,12 +1614,7 @@ def histogram(input, bins=100, min=0, max=0, name=None):
"""
if in_dygraph_mode():
return _C_ops.histogram(input, bins, min, max)
if _in_legacy_dygraph():
return _legacy_C_ops.histogram(
input, "bins", bins, "min", min, "max", max
)
else:
helper = LayerHelper('histogram', **locals())
check_variable_and_dtype(
input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram'
......@@ -1800,9 +1661,7 @@ def bincount(x, weights=None, minlength=0, name=None):
if in_dygraph_mode():
return _C_ops.bincount(x, weights, minlength)
elif _in_legacy_dygraph():
return _legacy_C_ops.bincount(x, weights, "minlength", minlength)
else:
helper = LayerHelper('bincount', **locals())
check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')
......@@ -1859,10 +1718,6 @@ def mv(x, vec, name=None):
if in_dygraph_mode():
return _C_ops.mv(x, vec)
else:
if _in_legacy_dygraph():
out = _legacy_C_ops.mv(x, vec)
return out
else:
def __check_input(x, vec):
var_names = {'x': x, 'vec': vec}
......@@ -1927,10 +1782,7 @@ def det(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.det(x)
if _in_legacy_dygraph():
return _legacy_C_ops.determinant(x)
else:
check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')
input_shape = list(x.shape)
......@@ -1989,10 +1841,7 @@ def slogdet(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.slogdet(x)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.slogdeterminant(x)
else:
check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')
input_shape = list(x.shape)
......@@ -2011,7 +1860,9 @@ def slogdet(x, name=None):
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]}
type='slogdeterminant',
inputs={'Input': [x]},
outputs={'Out': [out]},
)
return out
......@@ -2071,8 +1922,7 @@ def svd(x, full_matrices=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.svd(x, full_matrices)
if _in_legacy_dygraph():
return _legacy_C_ops.svd(x, 'full_matrices', full_matrices)
else:
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')
check_type(full_matrices, 'full_matrices', bool, 'svd')
helper = LayerHelper('svd', **locals())
......@@ -2146,11 +1996,10 @@ def matrix_power(x, n, name=None):
"""
if in_dygraph_mode():
return _C_ops.matrix_power(x, n)
if _in_legacy_dygraph():
return _legacy_C_ops.matrix_power(x, "n", n)
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')
else:
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64'], 'matrix_power'
)
check_type(n, 'n', int, 'matrix_power')
helper = LayerHelper('matrix_power', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -2211,12 +2060,7 @@ def qr(x, mode="reduced", name=None):
return r
else:
return q, r
if _in_legacy_dygraph():
q, r = _legacy_C_ops.qr(x, 'mode', mode)
if mode == "r":
return r
else:
return q, r
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')
check_type(mode, 'mode', str, 'qr')
helper = LayerHelper('qr', **locals())
......@@ -2315,8 +2159,6 @@ def lu(x, pivot=True, get_infos=False, name=None):
if in_dygraph_mode():
lu, p, info = _C_ops.lu(x, pivot)
elif paddle.in_dynamic_mode():
lu, p, info = _legacy_C_ops.lu(x, 'pivot', pivot)
else:
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')
helper = LayerHelper('lu', **locals())
......@@ -2413,14 +2255,10 @@ def lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):
if in_dygraph_mode():
P, L, U = _C_ops.lu_unpack(x, y, unpack_ludata, unpack_pivots)
return P, L, U
if paddle.in_dynamic_mode():
P, L, U = _legacy_C_ops.lu_unpack(
x, y, 'unpack_ludata', unpack_ludata, 'unpack_pivots', unpack_pivots
else:
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64'], 'lu_unpack'
)
return P, L, U
check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')
helper = LayerHelper('lu_unpack', **locals())
p = helper.create_variable_for_type_inference(dtype=x.dtype)
l = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -2486,10 +2324,7 @@ def eig(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.eig(x)
elif paddle.in_dynamic_mode():
w, v = _legacy_C_ops.eig(x)
return w, v
else:
check_variable_and_dtype(
x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig'
)
......@@ -2562,9 +2397,7 @@ def eigvals(x, name=None):
if in_dygraph_mode():
return _C_ops.eigvals(x)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.eigvals(x)
else:
helper = LayerHelper('eigvals', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})
......@@ -2627,11 +2460,9 @@ def multi_dot(x, name=None):
# [10, 7]
"""
if _in_legacy_dygraph():
return _legacy_C_ops.multi_dot(x)
if in_dygraph_mode():
return _C_ops.multi_dot(x)
else:
check_type(x, 'x', (list, tuple), 'multi_dot')
for id, item in enumerate(x):
check_variable_and_dtype(
......@@ -2648,7 +2479,9 @@ def multi_dot(x, name=None):
helper = LayerHelper('multi_dot', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='multi_dot', inputs={"X": x}, outputs={"Out": out})
helper.append_op(
type='multi_dot', inputs={"X": x}, outputs={"Out": out}
)
return out
......@@ -2687,9 +2520,7 @@ def eigh(x, UPLO='L', name=None):
"""
if in_dygraph_mode():
return _C_ops.eigh(x, UPLO)
if _in_legacy_dygraph():
return _legacy_C_ops.eigh(x, 'UPLO', UPLO)
else:
def __check_input(x, UPLO):
x_shape = list(x.shape)
......@@ -2713,7 +2544,10 @@ def eigh(x, UPLO='L', name=None):
helper = LayerHelper('eigh', **locals())
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh'
x,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'eigh',
)
out_value = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -2838,68 +2672,6 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
u_conj = _C_ops.conj(u)
out_2 = _C_ops.matmul(out_1, u_conj, False, True)
return out_2
if _in_legacy_dygraph():
if not hermitian:
# combine svd and matmul op
u, s, vt = _legacy_C_ops.svd(x, 'full_matrices', False)
max_singular_val = _legacy_C_ops.reduce_max(
s, 'dim', [-1], 'keep_dim', True, 'reduce_all', False
)
rcond = paddle.to_tensor(rcond, dtype=x.dtype)
cutoff = rcond * max_singular_val
y = float('inf')
y = paddle.to_tensor(y, dtype=x.dtype)
condition = s > cutoff
cond_int = cast(condition, s.dtype)
cond_not_int = cast(logical_not(condition), s.dtype)
out1 = multiply(1 / s, cond_int)
out2 = multiply(1 / y, cond_not_int)
singular = add(out1, out2)
st, _ = _legacy_C_ops.unsqueeze2(singular, 'axes', [-2])
dims = list(range(len(vt.shape)))
perm = dims[:-2] + [dims[-1]] + [dims[-2]]
v, _ = _legacy_C_ops.transpose2(vt, 'axis', perm)
out_1 = v * st
if in_dygraph_mode():
out_2 = _C_ops.matmul(out_1, u, False, True)
else:
out_2 = _legacy_C_ops.matmul_v2(
out_1, u, 'trans_x', False, 'trans_y', True
)
return out_2
else:
# combine eigh and matmul op
s, u = _legacy_C_ops.eigh(x, 'UPLO', 'L')
s_abs = paddle.abs(s)
max_singular_val = _legacy_C_ops.reduce_max(
s_abs, 'dim', [-1], 'keep_dim', True, 'reduce_all', False
)
rcond = paddle.to_tensor(rcond, dtype=s.dtype)
cutoff = rcond * max_singular_val
y = float('inf')
y = paddle.to_tensor(y, dtype=s.dtype)
condition = s_abs > cutoff
cond_int = cast(condition, s.dtype)
cond_not_int = cast(logical_not(condition), s.dtype)
out1 = multiply(1 / s, cond_int)
out2 = multiply(1 / y, cond_not_int)
singular = add(out1, out2)
st, _ = _legacy_C_ops.unsqueeze2(singular, 'axes', [-2])
out_1 = u * st
u_conj = _legacy_C_ops.conj(u)
if in_dygraph_mode():
out_2 = _C_ops.matmul(out_1, u_conj, False, True)
else:
out_2 = _legacy_C_ops.matmul_v2(
out_1, u_conj, 'trans_x', False, 'trans_y', True
)
return out_2
else:
if not hermitian:
helper = LayerHelper('pinv', **locals())
......@@ -3098,10 +2870,7 @@ def solve(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.solve(x, y)
if _in_legacy_dygraph():
return _legacy_C_ops.solve(x, y)
else:
inputs = {"X": [x], "Y": [y]}
helper = LayerHelper("solve", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')
......@@ -3170,23 +2939,15 @@ def triangular_solve(
"""
if in_dygraph_mode():
return _C_ops.triangular_solve(x, y, upper, transpose, unitriangular)
if paddle.in_dynamic_mode():
return _legacy_C_ops.triangular_solve(
x,
y,
'upper',
upper,
'transpose',
transpose,
'unitriangular',
unitriangular,
)
else:
inputs = {"X": [x], "Y": [y]}
helper = LayerHelper("triangular_solve", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')
check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')
check_variable_and_dtype(
x, 'x', ['float32', 'float64'], 'triangular_solve'
)
check_variable_and_dtype(
y, 'y', ['float32', 'float64'], 'triangular_solve'
)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
......@@ -3237,13 +2998,14 @@ def cholesky_solve(x, y, upper=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.cholesky_solve(x, y, upper)
if _in_legacy_dygraph():
return _legacy_C_ops.cholesky_solve(x, y, 'upper', upper)
else:
helper = LayerHelper("cholesky_solve", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')
check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')
check_variable_and_dtype(
x, 'x', ['float32', 'float64'], 'cholesky_solve'
)
check_variable_and_dtype(
y, 'y', ['float32', 'float64'], 'cholesky_solve'
)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
......@@ -3284,11 +3046,7 @@ def eigvalsh(x, UPLO='L', name=None):
if in_dygraph_mode():
values, _ = _C_ops.eigvalsh(x, UPLO, x.stop_gradient)
return values
elif paddle.in_dynamic_mode():
is_test = x.stop_gradient
values, _ = _legacy_C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)
return values
else:
def __check_input(x, UPLO):
x_shape = list(x.shape)
......@@ -3423,16 +3181,10 @@ def lstsq(x, y, rcond=None, driver=None, name=None):
elif x.dtype == paddle.float64:
rcond = 1e-15 * max(x.shape[-2], x.shape[-1])
if _non_static_mode():
if in_dygraph_mode():
solution, residuals, rank, singular_values = _C_ops.lstsq(
x, y, rcond, driver
)
else:
solution, residuals, rank, singular_values = _legacy_C_ops.lstsq(
x, y, 'rcond', rcond, 'driver', driver
)
if driver == "gels":
rank = paddle.empty(shape=[0], dtype=paddle.int32)
singular_values = paddle.empty(shape=[0], dtype=x.dtype)
......@@ -3440,19 +3192,27 @@ def lstsq(x, y, rcond=None, driver=None, name=None):
singular_values = paddle.empty(shape=[0], dtype=x.dtype)
return solution, residuals, rank, singular_values
else:
helper = LayerHelper('lstsq', **locals())
check_variable_and_dtype(
x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq'
x,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'lstsq',
)
check_variable_and_dtype(
y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq'
y,
'dtype',
['float32', 'float64', 'complex64', 'complex128'],
'lstsq',
)
solution = helper.create_variable_for_type_inference(dtype=x.dtype)
residuals = helper.create_variable_for_type_inference(dtype=x.dtype)
rank = helper.create_variable_for_type_inference(dtype=paddle.int32)
singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)
singular_values = helper.create_variable_for_type_inference(
dtype=x.dtype
)
helper.append_op(
type='lstsq',
......@@ -3468,9 +3228,13 @@ def lstsq(x, y, rcond=None, driver=None, name=None):
if driver == "gels":
rank = paddle.static.data(name='rank', shape=[0])
singular_values = paddle.static.data(name='singular_values', shape=[0])
singular_values = paddle.static.data(
name='singular_values', shape=[0]
)
elif driver == "gelsy":
singular_values = paddle.static.data(name='singular_values', shape=[0])
singular_values = paddle.static.data(
name='singular_values', shape=[0]
)
return solution, residuals, rank, singular_values
......
python/paddle/tensor/logic.py
浏览文件 @ 861fef52
......@@ -26,10 +26,9 @@ if _in_eager_mode_:
else:
from ..framework import VarBase as Tensor
from paddle import _C_ops, _legacy_C_ops
from paddle import _C_ops
from paddle.tensor.creation import full
from ..fluid.framework import _in_legacy_dygraph
from ..framework import LayerHelper, in_dygraph_mode
__all__ = []
......@@ -42,12 +41,7 @@ def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
return op(x, y)
else:
return op(x)
elif _in_legacy_dygraph():
op = getattr(_legacy_C_ops, op_name)
if binary_op:
return op(x, y)
else:
return op(x)
check_variable_and_dtype(
x,
"x",
......@@ -58,7 +52,15 @@ def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
check_variable_and_dtype(
y,
"y",
["bool", "int8", "int16", "int32", "int64", "float32", "float64"],
[
"bool",
"int8",
"int16",
"int32",
"int64",
"float32",
"float64",
],
op_name,
)
if out is not None:
......@@ -80,7 +82,9 @@ def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
type=op_name, inputs={"X": x, "Y": y}, outputs={"Out": out}
)
else:
helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out})
helper.append_op(
type=op_name, inputs={"X": x}, outputs={"Out": out}
)
return out
......@@ -288,9 +292,7 @@ def is_empty(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.is_empty(x)
if _in_legacy_dygraph():
return _legacy_C_ops.is_empty(x)
else:
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'is_empty'
)
......@@ -336,14 +338,13 @@ def equal_all(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.equal_all(x, y)
if paddle.in_dynamic_mode():
return _legacy_C_ops.equal_all(x, y)
else:
helper = LayerHelper("equal_all", **locals())
out = helper.create_variable_for_type_inference(dtype='bool')
helper.append_op(
type='equal_all', inputs={'X': [x], 'Y': [y]}, outputs={'Out': [out]}
type='equal_all',
inputs={'X': [x], 'Y': [y]},
outputs={'Out': [out]},
)
return out
......@@ -393,10 +394,7 @@ def allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None):
if in_dygraph_mode():
return _C_ops.allclose(x, y, rtol, atol, equal_nan)
if _in_legacy_dygraph():
return _legacy_C_ops.allclose(
x, y, 'rtol', str(rtol), 'atol', str(atol), 'equal_nan', equal_nan
)
else:
check_variable_and_dtype(x, "input", ['float32', 'float64'], 'allclose')
check_variable_and_dtype(y, "input", ['float32', 'float64'], 'allclose')
check_type(rtol, 'rtol', float, 'allclose')
......@@ -456,9 +454,6 @@ def equal(x, y, name=None):
if in_dygraph_mode():
return _C_ops.equal(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.equal(x, y)
else:
check_variable_and_dtype(
x,
......@@ -512,9 +507,6 @@ def greater_equal(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.greater_equal(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.greater_equal(x, y)
else:
check_variable_and_dtype(
x,
......@@ -568,9 +560,6 @@ def greater_than(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.greater_than(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.greater_than(x, y)
else:
check_variable_and_dtype(
x,
......@@ -625,9 +614,6 @@ def less_equal(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.less_equal(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.less_equal(x, y)
else:
check_variable_and_dtype(
x,
......@@ -682,9 +668,6 @@ def less_than(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.less_than(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.less_than(x, y)
else:
check_variable_and_dtype(
x,
......@@ -739,9 +722,6 @@ def not_equal(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.not_equal(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.not_equal(x, y)
else:
check_variable_and_dtype(
x,
......@@ -802,15 +782,12 @@ def _bitwise_op(op_name, x, y, out=None, name=None, binary_op=True):
return op(x, y)
else:
return op(x)
elif _in_legacy_dygraph():
op = getattr(_legacy_C_ops, op_name)
if binary_op:
return op(x, y)
else:
return op(x)
check_variable_and_dtype(
x, "x", ["bool", "uint8", "int8", "int16", "int32", "int64"], op_name
x,
"x",
["bool", "uint8", "int8", "int16", "int32", "int64"],
op_name,
)
if y is not None:
check_variable_and_dtype(
......@@ -834,7 +811,9 @@ def _bitwise_op(op_name, x, y, out=None, name=None, binary_op=True):
type=op_name, inputs={"X": x, "Y": y}, outputs={"Out": out}
)
else:
helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out})
helper.append_op(
type=op_name, inputs={"X": x}, outputs={"Out": out}
)
return out
......@@ -998,11 +977,7 @@ def isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None):
if in_dygraph_mode():
return _C_ops.isclose(x, y, rtol, atol, equal_nan)
if _in_legacy_dygraph():
return _legacy_C_ops.isclose(
x, y, 'rtol', str(rtol), 'atol', str(atol), 'equal_nan', equal_nan
)
else:
check_variable_and_dtype(x, "input", ['float32', 'float64'], 'isclose')
check_variable_and_dtype(y, "input", ['float32', 'float64'], 'isclose')
check_type(rtol, 'rtol', float, 'isclose')
......
python/paddle/tensor/manipulation.py
浏览文件 @ 861fef52
......@@ -19,17 +19,16 @@ from collections import Counter
import numpy as np
import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle import _C_ops
from paddle.utils.inplace_utils import inplace_apis_in_dygraph_only
from ..common_ops_import import _varbase_creator, fill_constant
from ..common_ops_import import fill_constant
from ..fluid.data_feeder import (
check_dtype,
check_type,
check_variable_and_dtype,
convert_dtype,
)
from ..fluid.framework import _in_legacy_dygraph, _non_static_mode
from ..fluid.layers import utils
from ..framework import (
LayerHelper,
......@@ -124,7 +123,7 @@ def tensor_array_to_tensor(input, axis=1, use_stack=False, name=None):
paddle.tensor.array.array_write(x1, i + 1, array)
output, output_index = paddle.tensor.manipulation.tensor_array_to_tensor(input=array)
"""
if _non_static_mode():
if in_dygraph_mode():
assert isinstance(
input, list
), "The 'input' in tensor_array_to_tensor must be list"
......@@ -136,7 +135,7 @@ def tensor_array_to_tensor(input, axis=1, use_stack=False, name=None):
np.array(list(map(lambda x: int(x.shape[axis]), input)))
)
return res, sizes
else:
check_type(input, 'input', (list, Variable), 'tensor_array_to_tensor')
if isinstance(input, list):
for i, input_x in enumerate(input):
......@@ -147,7 +146,9 @@ def tensor_array_to_tensor(input, axis=1, use_stack=False, name=None):
'tensor_array_to_tensor',
)
helper = LayerHelper('tensor_array_to_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
out_index = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='tensor_array_to_tensor',
......@@ -186,13 +187,7 @@ def cast(x, dtype):
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
return _C_ops.cast(x, dtype)
if _non_static_mode():
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
out = _legacy_C_ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
return out
else:
check_variable_and_dtype(
x,
'x',
......@@ -362,73 +357,6 @@ def slice(input, axes, starts, ends):
return _C_ops.slice(input, axes, starts, ends, infer_flags, [])
else:
if _in_legacy_dygraph():
attrs = ()
starts_tensor = None
ends_tensor = None
if isinstance(axes, (list, tuple)):
axes = list(axes)
if len(axes) == 0:
raise ValueError(
"Input axes should not be an empty list/tuple."
)
for i in range(len(axes)):
if axes[i] < 0:
axes[i] = max(0, axes[i] + len(input.shape))
else:
axes[i] = min(len(input.shape) - 1, axes[i])
else:
raise ValueError(
"Input axes must be a python list or tuple, but reveived {}".format(
type(axes)
)
)
infer_flags = list(1 for i in range(len(axes)))
tmp_tensor_type = Variable
if isinstance(starts, (list, tuple)):
starts = [
item.numpy().item(0)
if isinstance(item, tmp_tensor_type)
else item
for item in starts
]
attrs += ('starts', starts)
elif isinstance(starts, tmp_tensor_type):
starts_tensor = starts
starts.stop_gradient = True
infer_flags = list(-1 for i in range(len(axes)))
if isinstance(ends, (list, tuple)):
ends = [
item.numpy().item(0)
if isinstance(item, tmp_tensor_type)
else item
for item in ends
]
attrs += ('ends', ends)
elif isinstance(ends, tmp_tensor_type):
ends_tensor = ends
ends_tensor.stop_gradient = True
infer_flags = list(-1 for i in range(len(axes)))
return _legacy_C_ops.slice(
input,
starts_tensor,
ends_tensor,
None,
None,
'axes',
axes,
'infer_flags',
infer_flags,
*attrs,
)
if not isinstance(starts, (list, tuple, Variable)):
raise ValueError(
"Input starts must be an Variable, python list or tuple."
......@@ -452,7 +380,9 @@ def slice(input, axes, starts, ends):
elif isinstance(starts, (list, tuple)):
attrs['starts'] = []
if utils._contain_var(starts):
inputs['StartsTensorList'] = utils._convert_to_tensor_list(starts)
inputs['StartsTensorList'] = utils._convert_to_tensor_list(
starts
)
for i, dim in enumerate(starts):
if isinstance(dim, Variable):
attrs['starts'].append(-1)
......@@ -545,10 +475,6 @@ def transpose(x, perm, name=None):
if in_dygraph_mode():
return _C_ops.transpose(x, perm)
else:
if _in_legacy_dygraph():
out, _ = _legacy_C_ops.transpose2(x, 'axis', perm)
return out
check_variable_and_dtype(
x,
'x',
......@@ -625,14 +551,7 @@ def unstack(x, axis=0, num=None):
if num == 0:
return []
return _C_ops.unstack(x, axis, num)
if _non_static_mode():
if num is None:
num = x.shape[axis]
if num == 0:
return []
return _legacy_C_ops.unstack(x, num, 'axis', int(axis), 'num', num)
else:
helper = LayerHelper('unstack', **locals())
if num is None:
if axis is None or x.shape[axis] <= 0:
......@@ -959,12 +878,7 @@ def fill_(x, value):
"The type of 'value' must be int or float, but received %s."
% (type(value))
)
if in_dygraph_mode():
return _C_ops.fill_(x, value)
else:
return _legacy_C_ops.fill_any_(
x, "value_float", float(value), "value_int", int(value)
)
@dygraph_only
......@@ -992,12 +906,7 @@ def zero_(x):
print(tensor.tolist()) #[0, 0, 0, 0, 0]
"""
if in_dygraph_mode():
return _C_ops.fill_(x, 0.0)
else:
return _legacy_C_ops.fill_any_(
x, "value_float", 0.0, "value_int", int(0)
)
@dygraph_only
......@@ -1025,39 +934,11 @@ def fill_diagonal_(x, value, offset=0, wrap=False, name=None):
x.fill_diagonal_(1.0)
print(x.tolist()) #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]]
"""
helper = LayerHelper("fill_diagonal_", **locals())
check_type(x, 'X', (Variable), 'fill_diagonal_')
dtype = helper.input_dtype('x')
check_dtype(
dtype,
'X',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'fill_diagonal_',
)
check_type(value, 'value', (bool, int, float), 'fill_diagonal_')
check_type(wrap, 'wrap', (bool), 'fill_diagonal_')
inshape = x.shape
inshapeset = set(inshape)
assert len(inshape) >= 2, 'Tensor dims should >= 2 in fill_diagonal_ API'
if len(inshape) > 2:
assert (
len(inshapeset) == 1
), 'Tensor dims should be equal while input dims > 2 in fill_diagonal_ API'
if in_dygraph_mode():
if len(inshape) == 2:
if len(x.shape) == 2:
return _C_ops.fill_diagonal_(x, value, offset, wrap)
return _C_ops.fill_diagonal_(x, value, offset, True)
if len(inshape) == 2:
return _legacy_C_ops.fill_diagonal_(
x, 'value', value, 'offset', offset, 'wrap', wrap
)
return _legacy_C_ops.fill_diagonal_(
x, 'value', value, 'offset', offset, 'wrap', True
)
def _fill_diagonal_tensor_impl(x, y, offset=0, dim1=0, dim2=1, inplace=False):
inshape = x.shape
......@@ -1087,18 +968,8 @@ def _fill_diagonal_tensor_impl(x, y, offset=0, dim1=0, dim2=1, inplace=False):
y = y.reshape([1, -1])
if inplace:
if in_dygraph_mode():
return _C_ops.fill_diagonal_tensor_(x, y, offset, dim1, dim2)
else:
return _legacy_C_ops.fill_diagonal_tensor_(
x, y, 'offset', offset, 'dim1', dim1, 'dim2', dim2
)
if in_dygraph_mode():
return _C_ops.fill_diagonal_tensor(x, y, offset, dim1, dim2)
else:
return _legacy_C_ops.fill_diagonal_tensor(
x, y, 'offset', offset, 'dim1', dim1, 'dim2', dim2
)
def fill_diagonal_tensor_(x, y, offset=0, dim1=0, dim2=1, name=None):
......@@ -1248,17 +1119,7 @@ def concat(x, axis=0, name=None):
if not isinstance(input, Variable):
input = [t for t in input if t.shape.count(0) == 0]
return _C_ops.concat(input, axis)
if _in_legacy_dygraph():
if isinstance(axis, Variable):
axis = axis.numpy()
axis = axis.item(0)
if not isinstance(input, Variable):
input = [t for t in input if t.shape.count(0) == 0]
out = _varbase_creator()
_legacy_C_ops.concat(input, out, 'axis', axis)
return out
else:
check_type(input, 'input', (list, tuple, Variable), 'concat')
if not isinstance(input, Variable):
for id, x in enumerate(input):
......@@ -1295,7 +1156,9 @@ def concat(x, axis=0, name=None):
)
helper = LayerHelper('concat', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
if input[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
# NOTE(liym27): Don't remove this if branch!
......@@ -1304,7 +1167,8 @@ def concat(x, axis=0, name=None):
assert len(input) == 1, (
"If the elements of 'input' in concat are Variable(LoDTensorArray), "
"number of the elements must be 1, but received %s." % len(input)
"number of the elements must be 1, but received %s."
% len(input)
)
out_index = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
......@@ -1323,7 +1187,10 @@ def concat(x, axis=0, name=None):
attrs['axis'] = axis
helper.append_op(
type='concat', inputs=inputs, outputs={'Out': [out]}, attrs=attrs
type='concat',
inputs=inputs,
outputs={'Out': [out]},
attrs=attrs,
)
return out
......@@ -1358,11 +1225,9 @@ def broadcast_tensors(input, name=None):
"""
num_inputs = len(input)
if paddle.framework.in_dygraph_mode():
if in_dygraph_mode():
return _C_ops.broadcast_tensors(input)
if paddle.framework._non_static_mode():
return _legacy_C_ops.broadcast_tensors(input, num_inputs)
else:
check_type(input, 'input', (list, tuple), 'broadcast_tensors')
if num_inputs < 1:
raise TypeError(
......@@ -1428,7 +1293,10 @@ def broadcast_tensors(input, name=None):
inputs = {'X': input}
helper.append_op(
type='broadcast_tensors', inputs=inputs, outputs={'Out': out}, attrs={}
type='broadcast_tensors',
inputs=inputs,
outputs={'Out': out},
attrs={},
)
return out
......@@ -1465,10 +1333,7 @@ def flip(x, axis, name=None):
if in_dygraph_mode():
return _C_ops.flip(x, axis)
if paddle.in_dynamic_mode():
return _legacy_C_ops.flip(x, "axis", axis)
else:
helper = LayerHelper("flip", **locals())
check_type(x, 'X', (Variable), 'flip')
dtype = helper.input_dtype('x')
......@@ -1482,10 +1347,15 @@ def flip(x, axis, name=None):
if name is None:
out = helper.create_variable_for_type_inference(dtype)
else:
out = helper.create_variable(name=name, dtype=dtype, persistable=False)
out = helper.create_variable(
name=name, dtype=dtype, persistable=False
)
helper.append_op(
type="flip", inputs={"X": x}, outputs={"Out": out}, attrs={"axis": axis}
type="flip",
inputs={"X": x},
outputs={"Out": out},
attrs={"axis": axis},
)
return out
......@@ -1705,13 +1575,7 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
if in_dygraph_mode():
return _C_ops.flatten(x, start_axis, stop_axis)
if _in_legacy_dygraph():
dy_out, _ = _legacy_C_ops.flatten_contiguous_range(
x, 'start_axis', start_axis, 'stop_axis', stop_axis
)
return dy_out
else:
helper = LayerHelper('flatten', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
......@@ -1760,12 +1624,6 @@ def flatten_(x, start_axis=0, stop_axis=-1, name=None):
if in_dygraph_mode():
return _C_ops.flatten_(x, start_axis, stop_axis)
if _in_legacy_dygraph():
dy_out, _ = _legacy_C_ops.flatten_contiguous_range_(
x, 'start_axis', start_axis, 'stop_axis', stop_axis
)
return dy_out
def roll(x, shifts, axis=None, name=None):
"""
......@@ -1830,10 +1688,7 @@ def roll(x, shifts, axis=None, name=None):
if in_dygraph_mode():
return _C_ops.roll(x, shifts, axis)
if _in_legacy_dygraph():
return _legacy_C_ops.roll(x, 'axis', axis, 'shifts', shifts)
else:
helper = LayerHelper("roll", **locals())
check_type(axis, 'axis', (list, tuple), 'roll')
......@@ -1947,10 +1802,7 @@ def stack(x, axis=0, name=None):
if in_dygraph_mode():
return _C_ops.stack(x, axis)
if _in_legacy_dygraph():
return _legacy_C_ops.stack(x, 'axis', axis)
else:
if not isinstance(x, list) and not isinstance(x, tuple):
# NOTE:(zhiqiu) Only support Variable as input if the Variable is a LOD_TENSOR_ARRAY create by create_array, array_write, array_read, etc.
# In that case, Variable is array of tensors indeed.
......@@ -2055,7 +1907,7 @@ def split(x, num_or_sections, axis=0, name=None):
"""
input = x
dim = axis
if _non_static_mode():
if in_dygraph_mode():
num = None
attrs = ()
......@@ -2085,16 +1937,11 @@ def split(x, num_or_sections, axis=0, name=None):
"The type of 'num_or_sections' in split must be int, list or tuple in imperative mode, but "
"received %s." % (type(num_or_sections))
)
if in_dygraph_mode():
if isinstance(num_or_sections, int):
return _C_ops.split_with_num(input, num_or_sections, dim)
else:
return _C_ops.split(input, num_or_sections, dim)
elif _in_legacy_dygraph():
out = [_varbase_creator() for n in range(num)]
_legacy_C_ops.split(input, out, *attrs)
return out
else:
check_variable_and_dtype(
input,
'input',
......@@ -2110,7 +1957,9 @@ def split(x, num_or_sections, axis=0, name=None):
],
'split',
)
check_type(num_or_sections, 'num_or_sections', (list, int, tuple), 'split')
check_type(
num_or_sections, 'num_or_sections', (list, int, tuple), 'split'
)
check_type(dim, 'dim', (int, Variable), 'split')
if isinstance(dim, Variable):
check_dtype(dim.dtype, 'dim', ['int32', 'int64'], 'split')
......@@ -2119,7 +1968,9 @@ def split(x, num_or_sections, axis=0, name=None):
input_shape = input.shape
inputs = {'X': input}
attrs = {'num': num_or_sections if isinstance(num_or_sections, int) else 0}
attrs = {
'num': num_or_sections if isinstance(num_or_sections, int) else 0
}
def _get_SectionsTensorList(one_list):
tensor_list = []
......@@ -2137,7 +1988,9 @@ def split(x, num_or_sections, axis=0, name=None):
% idx
)
unk_dim_idx = idx
temp_out = helper.create_variable_for_type_inference('int32')
temp_out = helper.create_variable_for_type_inference(
'int32'
)
fill_constant(
[1], 'int32', dim_size, force_cpu=True, out=temp_out
)
......@@ -2180,7 +2033,9 @@ def split(x, num_or_sections, axis=0, name=None):
)
outs = [
helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
for i in range(num)
]
helper.append_op(
......@@ -2317,10 +2172,7 @@ def squeeze(x, axis=None, name=None):
axes = axis
if in_dygraph_mode():
return _C_ops.squeeze(input, axes)
if _in_legacy_dygraph():
out, _ = _legacy_C_ops.squeeze2(input, 'axes', axes)
return out
else:
helper = LayerHelper("squeeze", **locals())
check_variable_and_dtype(
input,
......@@ -2379,9 +2231,6 @@ def squeeze_(x, axis=None, name=None):
axes = axis
if in_dygraph_mode():
return _C_ops.squeeze_(input, axes)
if _in_legacy_dygraph():
out, _ = _legacy_C_ops.squeeze2_(input, 'axes', axes)
return out
def unique_consecutive(
......@@ -2473,26 +2322,7 @@ def unique_consecutive(
if len(outs) == 1:
return outs[0]
return tuple(outs)
elif paddle.in_dynamic_mode():
out, inverse, counts = _legacy_C_ops.unique_consecutive(
x,
'dtype',
attr_dtype,
'return_inverse',
return_inverse,
'return_counts',
return_counts,
'axis',
axis,
)
outs = [out]
if return_inverse:
outs.append(inverse)
if return_counts:
outs.append(counts)
if len(outs) == 1:
return outs[0]
return tuple(outs)
else:
check_variable_and_dtype(
x,
"input",
......@@ -2527,7 +2357,10 @@ def unique_consecutive(
if return_counts:
outs.append(counts)
helper.append_op(
type="unique_consecutive", inputs={"X": x}, attrs=attrs, outputs=outputs
type="unique_consecutive",
inputs={"X": x},
attrs=attrs,
outputs=outputs,
)
if len(outs) == 1:
return outs[0]
......@@ -2604,27 +2437,10 @@ def unique(
else:
axis = [axis]
attr_dtype = convert_np_dtype_to_dtype_(dtype)
if _non_static_mode():
if in_dygraph_mode():
out, indices, inverse, counts = _C_ops.unique(
x, return_index, return_inverse, return_counts, axis, attr_dtype
)
if _in_legacy_dygraph():
out, inverse, indices, counts = _legacy_C_ops.unique(
x,
'dtype',
attr_dtype,
'return_index',
return_index,
'return_inverse',
return_inverse,
'return_counts',
return_counts,
'axis',
axis,
"is_sorted",
True,
)
outs = [out]
if return_index:
outs.append(indices)
......@@ -2637,7 +2453,7 @@ def unique(
return outs[0]
return tuple(outs)
else:
check_variable_and_dtype(
x, "input", ['float32', 'float64', 'int32', 'int64'], 'unique'
)
......@@ -2741,7 +2557,7 @@ def unsqueeze(x, axis, name=None):
"""
input = x
axes = axis
if _non_static_mode():
if in_dygraph_mode():
if isinstance(axes, int):
axes = [axes]
elif isinstance(axes, Variable):
......@@ -2751,11 +2567,8 @@ def unsqueeze(x, axis, name=None):
item.numpy().item(0) if isinstance(item, Variable) else item
for item in axes
]
if _in_legacy_dygraph():
out, _ = _legacy_C_ops.unsqueeze2(input, 'axes', axes)
return out
return _C_ops.unsqueeze(input, axes)
else:
check_type(axes, 'axis/axes', (int, list, tuple, Variable), 'unsqueeze')
check_variable_and_dtype(
input,
......@@ -2818,10 +2631,7 @@ def unsqueeze_(x, axis, name=None):
item.numpy().item(0) if isinstance(item, Variable) else item
for item in axes
]
if in_dygraph_mode():
return _C_ops.unsqueeze_(input, axes)
out, _ = _legacy_C_ops.unsqueeze2_(input, 'axes', axes)
return out
def gather(x, index, axis=None, name=None):
......@@ -2874,16 +2684,19 @@ def gather(x, index, axis=None, name=None):
if in_dygraph_mode():
return _C_ops.gather(x, index, axis)
if _in_legacy_dygraph():
axis = axis.item() if isinstance(axis, paddle.Tensor) else axis
return _legacy_C_ops.gather(
x, index, None, "axis", axis, "overwrite", False
)
else:
check_variable_and_dtype(
x,
'x',
['float16', 'float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
[
'float16',
'float32',
'float64',
'int16',
'int32',
'int64',
'uint8',
],
'gather',
)
check_variable_and_dtype(index, 'index', ['int32', 'int64'], 'gather')
......@@ -2945,19 +2758,17 @@ def unbind(input, axis=0):
"""
if in_dygraph_mode():
return _C_ops.unbind(input, axis)
else:
if not isinstance(axis, (int)):
raise TypeError(
"The type of 'axis' must be int, but received %s." % (type(axis))
"The type of 'axis' must be int, but received %s."
% (type(axis))
)
if isinstance(axis, np.generic):
axis = np.asscalar(axis)
input_shape = input.shape
axis_ = axis if axis >= 0 else len(input_shape) + axis
num = input_shape[axis_]
if _in_legacy_dygraph():
return _legacy_C_ops.unbind(input, num, 'axis', axis)
helper = LayerHelper("unbind", **locals())
check_type(input, 'input', (Variable), 'unbind')
dtype = helper.input_dtype()
......@@ -2965,7 +2776,9 @@ def unbind(input, axis=0):
dtype, 'unbind', ['float32', 'float64', 'int32', 'int64'], 'unbind'
)
outs = [
helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
for i in range(num)
]
helper.append_op(
......@@ -3053,11 +2866,6 @@ def scatter(x, index, updates, overwrite=True, name=None):
"""
if in_dygraph_mode():
return _C_ops.scatter(x, index, updates, overwrite)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.scatter(
x, index, updates, 'overwrite', overwrite
)
else:
check_variable_and_dtype(
x,
......@@ -3083,9 +2891,7 @@ def scatter_(x, index, updates, overwrite=True, name=None):
Inplace version of ``scatter`` API, the output Tensor will be inplaced with input ``x``.
Please refer to :ref:`api_paddle_tensor_scatter`.
"""
if in_dygraph_mode():
return _C_ops.scatter_(x, index, updates, overwrite)
return _legacy_C_ops.scatter_(x, index, updates, 'overwrite', overwrite)
def scatter_nd_add(x, index, updates, name=None):
......@@ -3159,10 +2965,6 @@ def scatter_nd_add(x, index, updates, name=None):
"""
if in_dygraph_mode():
return _C_ops.scatter_nd_add(x, index, updates)
else:
if _in_legacy_dygraph():
op = getattr(_legacy_C_ops, 'scatter_nd_add')
return op(x, index, updates)
else:
if x.dtype != updates.dtype:
raise ValueError("x and updates must have same data type.")
......@@ -3307,11 +3109,10 @@ def tile(x, repeat_times, name=None):
repeat_times = repeat_times.numpy().tolist()
return _C_ops.tile(x, repeat_times)
if _in_legacy_dygraph():
return _legacy_C_ops.tile(x, 'repeat_times', repeat_times)
check_type(repeat_times, 'repeat_times', (list, tuple, Variable), 'tile')
else:
check_type(
repeat_times, 'repeat_times', (list, tuple, Variable), 'tile'
)
if isinstance(repeat_times, Variable):
assert (
len(repeat_times.shape) == 1
......@@ -3404,12 +3205,12 @@ def expand_as(x, y, name=None):
"""
if in_dygraph_mode():
return _C_ops.expand_as(x, None, y.shape)
if _non_static_mode():
return _legacy_C_ops.expand_as_v2(x, 'target_shape', y.shape)
else:
check_variable_and_dtype(
x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'expand_as'
x,
'x',
['bool', 'float32', 'float64', 'int32', 'int64'],
'expand_as',
)
check_type(y, 'y', Variable, 'expand_as')
......@@ -3463,9 +3264,7 @@ def broadcast_to(x, shape, name=None):
"""
if in_dygraph_mode():
return _C_ops.expand(x, shape)
if _in_legacy_dygraph():
return _legacy_C_ops.expand_v2(x, 'shape', shape)
else:
if isinstance(shape, Variable):
assert len(shape.shape) == 1, 'shape must be an 1-D Tensor.'
else:
......@@ -3481,7 +3280,10 @@ def broadcast_to(x, shape, name=None):
), 'Elements in shape must be 1-D Tensors or integers.'
check_variable_and_dtype(
x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'broadcast_to'
x,
'x',
['bool', 'float32', 'float64', 'int32', 'int64'],
'broadcast_to',
)
check_type(shape, 'shape', (list, tuple, Variable), 'broadcast_to')
if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
......@@ -3557,10 +3359,7 @@ def expand(x, shape, name=None):
"""
if in_dygraph_mode():
return _C_ops.expand(x, shape)
if paddle.in_dynamic_mode():
return _legacy_C_ops.expand_v2(x, 'shape', shape)
else:
if isinstance(shape, Variable):
assert len(shape.shape) == 1, 'shape must be an 1-D Tensor.'
else:
......@@ -3710,25 +3509,6 @@ def reshape(x, shape, name=None):
return out
else:
if _in_legacy_dygraph():
tmp_tensor_type = Variable
if isinstance(shape, (list, tuple)):
shape = [
item.numpy().item(0) if isinstance(item, Variable) else item
for item in shape
]
out, _ = _legacy_C_ops.reshape2(x, None, 'shape', shape)
elif isinstance(shape, tmp_tensor_type):
shape.stop_gradient = True
out, _ = _legacy_C_ops.reshape2(x, shape)
else:
raise ValueError(
"shape must be an instance of `list`, `tuple` or `Variable`,"
" got '{}.'".format(type(shape))
)
return out
check_variable_and_dtype(
x,
'x',
......@@ -3745,7 +3525,9 @@ def reshape(x, shape, name=None):
'reshape',
)
check_type(shape, 'shape', (list, tuple, Variable), 'reshape')
check_type(actual_shape, 'actual_shape', (Variable, type(None)), 'reshape')
check_type(
actual_shape, 'actual_shape', (Variable, type(None)), 'reshape'
)
helper = LayerHelper("reshape2", **locals())
......@@ -3844,24 +3626,6 @@ def reshape_(x, shape, name=None):
)
return out
else:
if isinstance(shape, (list, tuple)):
shape = [
item.numpy().item(0) if isinstance(item, Variable) else item
for item in shape
]
out, _ = _legacy_C_ops.reshape2_(x, None, 'shape', shape)
return out
elif isinstance(shape, Variable):
shape.stop_gradient = True
# NOTE(pangyoki): Cannot support the case where the shape Tensor
# is negative. In the infer_shape stage, the input's dim will
# be changed to a negative number.
# Thus, convert Shape Tensor to list firstly and then call
# reshape inplace op.
shape_list = shape.numpy().tolist()
out, _ = _legacy_C_ops.reshape2_(x, None, 'shape', shape_list)
return out
def gather_nd(x, index, name=None):
......@@ -3939,15 +3703,15 @@ def gather_nd(x, index, name=None):
if in_dygraph_mode():
return _C_ops.gather_nd(x, index)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.gather_nd(x, index)
check_variable_and_dtype(
x,
'x',
['bool', 'float32', 'float64', 'int16', 'int32', 'int64'],
'gather_np',
)
check_variable_and_dtype(index, 'index', ['int32', 'int64'], 'gather_np')
check_variable_and_dtype(
index, 'index', ['int32', 'int64'], 'gather_np'
)
helper = LayerHelper('gather_nd', **locals())
dtype = helper.input_dtype()
output = helper.create_variable_for_type_inference(dtype)
......@@ -4043,7 +3807,7 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
"""
if in_dygraph_mode():
return _C_ops.strided_slice(x, axes, starts, ends, strides)
else:
helper = LayerHelper('strided_slice', **locals())
check_variable_and_dtype(
......@@ -4066,7 +3830,9 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
for i, var in enumerate(list_input):
var_name = input_name + '[' + str(i) + ']'
if isinstance(var, Variable):
check_dtype(var.dtype, var_name, ['int32'], 'strided_slice')
check_dtype(
var.dtype, var_name, ['int32'], 'strided_slice'
)
check_list_elements_dtype(axes, 'axes')
check_list_elements_dtype(starts, 'starts')
......@@ -4081,25 +3847,18 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
new_list_tensor.append(dim)
else:
assert isinstance(dim, int)
temp_out = helper.create_variable_for_type_inference('int32')
fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
temp_out = helper.create_variable_for_type_inference(
'int32'
)
fill_constant(
[1], 'int32', dim, force_cpu=True, out=temp_out
)
new_list_tensor.append(temp_out)
return new_list_tensor
inputs = {'Input': x}
attrs = {'axes': axes}
infer_flags = list(1 for i in range(len(axes)))
if _in_legacy_dygraph():
inputs = {'Input': x}
attrs = {
'axes': axes,
'starts': starts,
'ends': ends,
'strides': strides,
'infer_flags': infer_flags,
}
else:
# starts
if isinstance(starts, Variable):
starts.stop_gradient = True
......@@ -4155,7 +3914,10 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
dtype=helper.input_dtype('x')
)
helper.append_op(
type='strided_slice', inputs=inputs, attrs=attrs, outputs={'Out': out}
type='strided_slice',
inputs=inputs,
attrs=attrs,
outputs={'Out': out},
)
return out
......@@ -4281,7 +4043,7 @@ def tensordot(x, y, axes=2, name=None):
check_type(axes, 'axes', (int, tuple, list, Variable), op_type)
def _var_to_list(var):
if paddle.in_dynamic_mode():
if in_dygraph_mode():
return tolist(var)
raise TypeError(
"The 'axes' with type 'Tensor' in "
......@@ -4409,9 +4171,7 @@ def as_complex(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.as_complex(x)
if _in_legacy_dygraph():
return _legacy_C_ops.as_complex(x)
else:
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'as_complex')
op_type = "as_complex"
helper = LayerHelper(op_type, **locals())
......@@ -4421,7 +4181,9 @@ def as_complex(x, name=None):
)
outputs = {"Out": out}
attrs = {}
helper.append_op(type=op_type, inputs=inputs, attrs=attrs, outputs=outputs)
helper.append_op(
type=op_type, inputs=inputs, attrs=attrs, outputs=outputs
)
return out
......@@ -4462,9 +4224,7 @@ def as_real(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.as_real(x)
if _in_legacy_dygraph():
return _legacy_C_ops.as_real(x)
else:
check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'as_real')
op_type = "as_real"
helper = LayerHelper(op_type, **locals())
......@@ -4633,11 +4393,7 @@ def moveaxis(x, source, destination, name=None):
if in_dygraph_mode():
out = _C_ops.transpose(x, perm)
return out
if _in_legacy_dygraph():
out, _ = _legacy_C_ops.transpose2(x, 'axis', perm)
return out
else:
check_variable_and_dtype(
x,
'x',
......@@ -4727,15 +4483,14 @@ def take_along_axis(arr, indices, axis):
if not broadcast_shape:
# if indices matrix have larger size than arr, arr should broadcast into indices shape.
broadcast_shape = indices.shape
if _non_static_mode():
if in_dygraph_mode():
indices = paddle.broadcast_to(indices, broadcast_shape)
broadcast_shape_list = list(broadcast_shape)
broadcast_shape_list[axis] = list(arr.shape)[axis]
broadcast_shape = tuple(broadcast_shape_list)
arr = paddle.broadcast_to(arr, broadcast_shape)
if not _in_legacy_dygraph():
return _C_ops.take_along_axis(arr, indices, axis)
return _legacy_C_ops.take_along_axis(arr, indices, 'Axis', axis)
else:
check_variable_and_dtype(
arr,
'x',
......@@ -4797,7 +4552,7 @@ def put_along_axis(arr, indices, values, axis, reduce='assign'):
)
axis = non_negative_axis(arr, axis)
broadcast_shape = infer_broadcast_shape(arr, indices, axis)
if _non_static_mode():
if in_dygraph_mode():
values = (
paddle.to_tensor(values)
if not isinstance(values, paddle.Tensor)
......@@ -4806,12 +4561,8 @@ def put_along_axis(arr, indices, values, axis, reduce='assign'):
if broadcast_shape:
indices = paddle.broadcast_to(indices, broadcast_shape)
values = paddle.broadcast_to(values, indices.shape)
if in_dygraph_mode():
return _C_ops.put_along_axis(arr, indices, values, axis, reduce)
return _legacy_C_ops.put_along_axis(
arr, indices, values, "Axis", axis, "Reduce", reduce
)
else:
check_variable_and_dtype(
arr,
'x',
......@@ -4856,11 +4607,7 @@ def put_along_axis_(arr, indices, values, axis, reduce='assign'):
if broadcast_shape:
indices = paddle.broadcast_to(indices, broadcast_shape)
values = paddle.broadcast_to(values, indices.shape)
if in_dygraph_mode():
return _C_ops.put_along_axis_(arr, indices, values, axis, reduce)
return _legacy_C_ops.put_along_axis_(
arr, indices, values, "Axis", axis, "Reduce", reduce
)
def index_add(x, index, axis, value, name=None):
......
python/paddle/tensor/math.py
浏览文件 @ 861fef52
......@@ -34,9 +34,6 @@ from ..fluid.data_feeder import (
from ..fluid.layers import utils
from ..framework import (
LayerHelper,
_in_legacy_dygraph,
_non_static_mode,
_varbase_creator,
convert_np_dtype_to_dtype_,
core,
in_dygraph_mode,
......@@ -158,9 +155,7 @@ def log(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.log(x)
if _in_legacy_dygraph():
return _legacy_C_ops.log(x)
else:
check_variable_and_dtype(x, 'x', ['float32', 'float64'], "log")
inputs = {'X': [x]}
helper = LayerHelper('log', **locals())
......@@ -220,19 +215,7 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
if in_dygraph_mode():
out = _C_ops.scale(x, scale, float(bias), bias_after_scale)
return dygraph_utils._append_activation_in_dygraph(out, act)
elif _in_legacy_dygraph():
_scale = scale.numpy().item(0) if isinstance(scale, Variable) else scale
out = _legacy_C_ops.scale(
x,
'scale',
float(_scale),
'bias',
float(bias),
'bias_after_scale',
bias_after_scale,
)
return dygraph_utils._append_activation_in_dygraph(out, act)
else:
check_variable_and_dtype(
x,
"x",
......@@ -295,10 +278,12 @@ def stanh(x, scale_a=0.67, scale_b=1.7159, name=None):
"""
if _non_static_mode():
if in_dygraph_mode():
return _legacy_C_ops.stanh(x, 'scale_a', scale_a, 'scale_b', scale_b)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'stanh')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'stanh'
)
helper = LayerHelper('stanh', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -363,9 +348,7 @@ def multiplex(inputs, index, name=None):
"""
if in_dygraph_mode():
return _C_ops.multiplex(inputs, index)
elif _in_legacy_dygraph():
return _legacy_C_ops.multiplex(index, inputs)
else:
helper = LayerHelper('multiplex', **locals())
check_type(inputs, 'inputs', (list), 'multiplex')
......@@ -380,7 +363,9 @@ def multiplex(inputs, index, name=None):
['float32', 'float64', 'int32', 'int64'],
'multiplex',
)
check_variable_and_dtype(index, "index", ['int32', 'int64'], 'multiplex')
check_variable_and_dtype(
index, "index", ['int32', 'int64'], 'multiplex'
)
out = helper.create_variable_for_type_inference(inputs[0].dtype)
helper.append_op(
......@@ -399,17 +384,6 @@ def scale_(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
"""
if in_dygraph_mode():
return _C_ops.scale_(x, scale, float(bias), bias_after_scale)
if _in_legacy_dygraph():
_scale = scale.numpy().item(0) if isinstance(scale, Variable) else scale
return _legacy_C_ops.scale_(
x,
'scale',
float(_scale),
'bias',
float(bias),
'bias_after_scale',
bias_after_scale,
)
def pow(x, y, name=None):
......@@ -469,17 +443,7 @@ def pow(x, y, name=None):
raise TypeError(
'y must be scalar or tensor type, but received: %s ' % (y.dtype)
)
if _in_legacy_dygraph():
if isinstance(y, (int, float)):
return _legacy_C_ops.pow(x, 'factor', y)
elif isinstance(y, (paddle.Tensor, Variable)):
return _elementwise_op_in_dygraph(
x, y, axis=-1, act=None, op_name='elementwise_pow'
)
else:
raise TypeError(
'y must be scalar or tensor type, but received: %s ' % (y.dtype)
)
# in static graph mode
if isinstance(y, (int, float)):
helper = LayerHelper('pow', **locals())
......@@ -531,11 +495,6 @@ def _elementwise_op_in_dygraph(
OP_NAMEMAPPING[op_name] if not is_inplace(op_name) else op_name,
)
out = op(x, y)
if _in_legacy_dygraph():
op = getattr(_legacy_C_ops, op_name)
out = op(x, y, 'axis', axis, 'use_mkldnn', use_mkldnn)
return dygraph_utils._append_activation_in_dygraph(
out, act, use_mkldnn=use_mkldnn
)
......@@ -642,9 +601,6 @@ def add(x, y, name=None):
if in_dygraph_mode():
return _C_ops.add(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.elementwise_add(x, y)
else:
return _elementwise_op(LayerHelper('elementwise_add', **locals()))
......@@ -734,11 +690,6 @@ def subtract(x, y, name=None):
act = None
if in_dygraph_mode():
return _C_ops.subtract(x, y)
else:
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -806,11 +757,6 @@ def divide(x, y, name=None):
act = None
if in_dygraph_mode():
return _C_ops.divide(x, y)
else:
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -853,9 +799,7 @@ def floor_divide(x, y, name=None):
axis = -1
if in_dygraph_mode():
return _C_ops.floor_divide(x, y)
elif _in_legacy_dygraph():
return _elementwise_op_in_dygraph(x, y, axis=axis, op_name=op_type)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -897,9 +841,7 @@ def remainder(x, y, name=None):
if in_dygraph_mode():
return _C_ops.remainder(x, y)
elif _in_legacy_dygraph():
return _elementwise_op_in_dygraph(x, y, axis=axis, op_name=op_type)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -970,11 +912,6 @@ def multiply(x, y, name=None):
if in_dygraph_mode():
return _C_ops.multiply(x, y)
else:
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
if x.dtype != y.dtype:
raise TypeError(
......@@ -1017,11 +954,6 @@ def _add_with_axis(x, y, axis=-1, name=None):
else:
op_type = 'elementwise_add'
act = None
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1034,11 +966,6 @@ def _subtract_with_axis(x, y, axis=-1, name=None):
else:
op_type = 'elementwise_sub'
act = None
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1051,11 +978,6 @@ def _multiply_with_axis(x, y, axis=-1, name=None):
else:
op_type = 'elementwise_mul'
act = None
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1066,11 +988,6 @@ def _divide_with_axis(x, y, axis=-1, name=None):
else:
op_type = 'elementwise_div'
act = None
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1135,10 +1052,7 @@ def maximum(x, y, name=None):
act = None
if in_dygraph_mode():
return _C_ops.maximum(x, y)
elif _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1203,10 +1117,7 @@ def minimum(x, y, name=None):
act = None
if in_dygraph_mode():
return _C_ops.minimum(x, y)
elif _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1273,10 +1184,7 @@ def fmax(x, y, name=None):
act = None
if in_dygraph_mode():
return _C_ops.fmax(x, y)
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1343,10 +1251,7 @@ def fmin(x, y, name=None):
act = None
if in_dygraph_mode():
return _C_ops.fmin(x, y)
if _in_legacy_dygraph():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -1417,35 +1322,8 @@ def sum(x, axis=None, dtype=None, keepdim=False, name=None):
if in_dygraph_mode():
return _C_ops.sum(x, axis, dtype, keepdim)
reduce_all, axis = _get_reduce_axis_with_tensor(axis, x)
if _in_legacy_dygraph():
if dtype_flag:
return _legacy_C_ops.reduce_sum(
x,
'dim',
axis,
'keep_dim',
keepdim,
'reduce_all',
reduce_all,
'in_dtype',
x.dtype,
'out_dtype',
dtype,
)
else:
return _legacy_C_ops.reduce_sum(
x,
'dim',
axis,
'keep_dim',
keepdim,
'reduce_all',
reduce_all,
)
reduce_all, axis = _get_reduce_axis_with_tensor(axis, x)
attrs = {'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all}
if dtype_flag:
......@@ -1468,7 +1346,9 @@ def sum(x, axis=None, dtype=None, keepdim=False, name=None):
'sum',
)
check_type(axis, 'axis', (int, list, tuple, type(None), Variable), 'sum')
check_type(
axis, 'axis', (int, list, tuple, type(None), Variable), 'sum'
)
helper = LayerHelper('sum', **locals())
if dtype_flag:
......@@ -1476,7 +1356,10 @@ def sum(x, axis=None, dtype=None, keepdim=False, name=None):
else:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reduce_sum', inputs={'X': x}, outputs={'Out': out}, attrs=attrs
type='reduce_sum',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs,
)
return out
......@@ -1784,11 +1667,7 @@ def add_n(inputs, name=None):
if isinstance(inputs, Variable):
inputs = [inputs]
return _C_ops.add_n(inputs)
if _in_legacy_dygraph():
if isinstance(inputs, Variable):
inputs = [inputs]
return _legacy_C_ops.sum(inputs, 'use_mkldnn', False)
else:
helper = LayerHelper('add_n', **locals())
check_type(inputs, 'inputs', (Variable, tuple, list), 'add_n')
if isinstance(inputs, list) or isinstance(inputs, tuple):
......@@ -1851,9 +1730,6 @@ def trunc(input, name=None):
'''
if in_dygraph_mode():
return _C_ops.trunc(input)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.trunc(input)
else:
inputs = {"X": input}
attrs = {}
......@@ -1939,8 +1815,7 @@ def mm(input, mat2, name=None):
"""
if in_dygraph_mode():
return _C_ops.matmul(input, mat2, False, False)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.matmul_v2(input, mat2)
else:
def __check_input(x, y):
var_names = {'x': x, 'y': y}
......@@ -1983,7 +1858,9 @@ def mm(input, mat2, name=None):
helper = LayerHelper('mm', **locals())
out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='matmul_v2', inputs={'X': input, 'Y': mat2}, outputs={'Out': out}
type='matmul_v2',
inputs={'X': input, 'Y': mat2},
outputs={'Out': out},
)
return out
......@@ -2079,10 +1956,6 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
if in_dygraph_mode():
return _C_ops.addmm(input, x, y, beta, alpha)
else:
if _in_legacy_dygraph():
out = _legacy_C_ops.addmm(input, x, y, "Alpha", alpha, "Beta", beta)
return out
else:
inputs = {'Input': input, "X": x, "Y": y}
attrs = {'Alpha': alpha, 'Beta': beta}
......@@ -2154,12 +2027,7 @@ def renorm(x, p, axis, max_norm):
if in_dygraph_mode():
out = _C_ops.renorm(x, p, axis, max_norm)
return out
elif _in_legacy_dygraph():
out = _legacy_C_ops.renorm(
x, 'p', p, 'axis', axis, 'max_norm', max_norm
)
return out
else:
inputs = {'X': x}
attrs = {'p': p, 'axis': axis, 'max_norm': max_norm}
......@@ -2213,8 +2081,7 @@ def inner(x, y, name=None):
if in_dygraph_mode():
return _C_ops.matmul(nx, ny.T, False, False).reshape(dstshape)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.matmul_v2(nx, ny.T).reshape(dstshape)
else:
def __check_input(x, y):
var_names = {'x': x, 'y': y}
......@@ -2240,7 +2107,9 @@ def inner(x, y, name=None):
helper = LayerHelper('inner', **locals())
out = helper.create_variable_for_type_inference(dtype=nx.dtype)
helper.append_op(
type='matmul_v2', inputs={'X': nx, 'Y': ny.T}, outputs={'Out': out}
type='matmul_v2',
inputs={'X': nx, 'Y': ny.T},
outputs={'Out': out},
)
return out.reshape(dstshape)
......@@ -2279,8 +2148,7 @@ def outer(x, y, name=None):
if in_dygraph_mode():
return _C_ops.matmul(nx, ny, False, False)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.matmul_v2(nx, ny)
else:
def __check_input(x, y):
var_names = {'x': x, 'y': y}
......@@ -2345,11 +2213,7 @@ def logsumexp(x, axis=None, keepdim=False, name=None):
if in_dygraph_mode():
return _C_ops.logsumexp(x, axis, keepdim, reduce_all)
if _in_legacy_dygraph():
return _legacy_C_ops.logsumexp(
x, 'axis', axis, 'keepdim', keepdim, 'reduce_all', reduce_all
)
else:
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'logsumexp')
helper = LayerHelper('logsumexp', **locals())
......@@ -2390,8 +2254,7 @@ def inverse(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.inverse(x)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.inverse(x)
else:
def _check_input(x):
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'inverse')
......@@ -2491,12 +2354,8 @@ def max(x, axis=None, keepdim=False, name=None):
if in_dygraph_mode():
return _C_ops.max(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis_with_tensor(axis, x)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_max(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
helper = LayerHelper('max', **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'max'
......@@ -2593,13 +2452,8 @@ def min(x, axis=None, keepdim=False, name=None):
if in_dygraph_mode():
return _C_ops.min(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis_with_tensor(axis, x)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_min(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
helper = LayerHelper('min', **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'min'
......@@ -2707,12 +2561,8 @@ def amax(x, axis=None, keepdim=False, name=None):
if in_dygraph_mode():
return _C_ops.amax(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis(axis, x)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_amax(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
helper = LayerHelper('amax', **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'amax'
......@@ -2821,11 +2671,8 @@ def amin(x, axis=None, keepdim=False, name=None):
if in_dygraph_mode():
return _C_ops.amin(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis(axis, x)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_amin(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
helper = LayerHelper('amin', **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'amin'
......@@ -2867,9 +2714,7 @@ def log1p(x, name=None):
if in_dygraph_mode():
return _C_ops.log1p(x)
if _in_legacy_dygraph():
return _legacy_C_ops.log1p(x)
else:
check_variable_and_dtype(x, 'x', ['float32', 'float64'], "log1p")
inputs = {'X': [x]}
helper = LayerHelper('log1p', **locals())
......@@ -2919,10 +2764,10 @@ def log2(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.log2(x)
if _in_legacy_dygraph():
return _legacy_C_ops.log2(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], "log2")
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], "log2"
)
inputs = {'X': [x]}
helper = LayerHelper('log2', **locals())
dtype = helper.input_dtype(input_param_name='x')
......@@ -2971,10 +2816,10 @@ def log10(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.log10(x)
if _in_legacy_dygraph():
return _legacy_C_ops.log10(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], "log10")
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], "log10"
)
inputs = {'X': [x]}
helper = LayerHelper('log10', **locals())
dtype = helper.input_dtype(input_param_name='x')
......@@ -3038,16 +2883,7 @@ def clip(x, min=None, max=None, name=None):
min = min_ if min is None else min
max = max_ if max is None else max
return _C_ops.clip(x, min, max)
if _in_legacy_dygraph():
if isinstance(min, Variable):
min = min.numpy().item(0)
if isinstance(max, Variable):
max = max.numpy().item(0)
min = min_ if min is None else min
max = max_ if max is None else max
return _legacy_C_ops.clip(x, "min", min, "max", max)
else:
if min is not None:
check_type(min, 'min', (float, int, Variable), 'clip')
if isinstance(min, Variable):
......@@ -3117,9 +2953,6 @@ def clip_(x, min=None, max=None, name=None):
if in_dygraph_mode():
return _C_ops.clip_(x, min, max)
if _in_legacy_dygraph():
return _legacy_C_ops.clip_(x, "min", min, "max", max)
def trace(x, offset=0, axis1=0, axis2=1, name=None):
"""
......@@ -3196,12 +3029,7 @@ def trace(x, offset=0, axis1=0, axis2=1, name=None):
if in_dygraph_mode():
return _C_ops.trace(x, offset, axis1, axis2)
if _in_legacy_dygraph():
return _legacy_C_ops.trace(
x, 'offset', offset, 'axis1', axis1, 'axis2', axis2
)
else:
__check_input(x, offset, axis1, axis2)
helper = LayerHelper('trace', **locals())
......@@ -3284,10 +3112,6 @@ def diagonal(x, offset=0, axis1=0, axis2=1, name=None):
if in_dygraph_mode():
return _C_ops.diagonal(x, offset, axis1, axis2)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.diagonal(
x, 'offset', offset, 'axis1', axis1, 'axis2', axis2
)
def __check_input(x, offset, axis1, axis2):
check_dtype(
......@@ -3363,10 +3187,9 @@ def kron(x, y, name=None):
# [12, 15, 18, 16, 20, 24],
# [21, 24, 27, 28, 32, 36]])
"""
if _in_legacy_dygraph():
return _legacy_C_ops.kron(x, y)
if in_dygraph_mode():
return _C_ops.kron(x, y)
return _legacy_C_ops.kron(x, y)
else:
helper = LayerHelper('kron', **locals())
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'kron'
......@@ -3376,7 +3199,9 @@ def kron(x, y, name=None):
)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="kron", inputs={"X": x, "Y": y}, outputs={"Out": out})
helper.append_op(
type="kron", inputs={"X": x, "Y": y}, outputs={"Out": out}
)
return out
......@@ -3432,12 +3257,7 @@ def cumsum(x, axis=None, dtype=None, name=None):
if axis is None:
axis = -1
return _C_ops.cumsum(x, axis, flatten, False, False)
if _in_legacy_dygraph():
if axis is None:
return _legacy_C_ops.cumsum(x, 'flatten', flatten)
else:
return _legacy_C_ops.cumsum(x, 'axis', axis, 'flatten', flatten)
check_type(x, 'x', (Variable), 'cumsum')
locals_var = locals().copy()
kwargs = dict()
......@@ -3507,14 +3327,7 @@ def logcumsumexp(x, axis=None, dtype=None, name=None):
if axis is None:
axis = -1
return _C_ops.logcumsumexp(x, axis, flatten, False, False)
if _in_legacy_dygraph():
if axis is None:
return _legacy_C_ops.logcumsumexp(x, 'flatten', flatten)
else:
return _legacy_C_ops.logcumsumexp(
x, 'axis', axis, 'flatten', flatten
)
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], "logcumsumexp"
)
......@@ -3586,9 +3399,7 @@ def cumprod(x, dim=None, dtype=None, name=None):
if in_dygraph_mode():
return _C_ops.cumprod(x, dim)
if _in_legacy_dygraph():
return _legacy_C_ops.cumprod(x, 'dim', dim)
else:
check_variable_and_dtype(
x,
"x",
......@@ -3631,14 +3442,18 @@ def isfinite(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.isfinite(x)
if _in_legacy_dygraph():
return _legacy_C_ops.isfinite_v2(x)
else:
helper = LayerHelper("isfinite_v2", **locals())
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isfinite'
x,
'x',
['float16', 'float32', 'float64', 'int32', 'int64'],
'isfinite',
)
out = helper.create_variable_for_type_inference('bool')
helper.append_op(type="isfinite_v2", inputs={"X": x}, outputs={"Out": out})
helper.append_op(
type="isfinite_v2", inputs={"X": x}, outputs={"Out": out}
)
return out
......@@ -3665,8 +3480,7 @@ def isinf(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.isinf(x)
if _in_legacy_dygraph():
return _legacy_C_ops.isinf_v2(x)
else:
helper = LayerHelper("isinf_v2", **locals())
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isinf'
......@@ -3699,9 +3513,7 @@ def isnan(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.isnan(x)
if _in_legacy_dygraph():
return _legacy_C_ops.isnan_v2(x)
else:
helper = LayerHelper("isnan_v2", **locals())
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isnan'
......@@ -3775,17 +3587,17 @@ def prod(x, axis=None, keepdim=False, dtype=None, name=None):
reduce_all, axis = _get_reduce_axis_with_tensor(axis, x)
if in_dygraph_mode():
return _C_ops.prod(x, axis, keepdim, reduce_all)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_prod(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
else:
helper = LayerHelper('reduce_prod', **locals())
check_variable_and_dtype(
x, 'x/input', ['float32', 'float64', 'int32', 'int64'], 'reduce_prod'
x,
'x/input',
['float32', 'float64', 'int32', 'int64'],
'reduce_prod',
)
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='reduce_prod',
inputs={'X': x},
......@@ -3817,11 +3629,10 @@ def sign(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.sign(x)
if _in_legacy_dygraph():
return _legacy_C_ops.sign(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'sign')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'sign'
)
helper = LayerHelper("sign", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -3857,11 +3668,10 @@ def tanh(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.tanh(x)
if _in_legacy_dygraph():
return _legacy_C_ops.tanh(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'tanh')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'tanh'
)
check_type(x, 'x', (Variable), 'tanh')
helper = LayerHelper('tanh', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
......@@ -3875,9 +3685,7 @@ def tanh_(x, name=None):
Inplace version of ``tanh`` API, the output Tensor will be inplaced with input ``x``.
Please refer to :ref:`api_tensor_tanh`.
"""
if in_dygraph_mode():
return _C_ops.tanh_(x)
return _legacy_C_ops.tanh_(x)
def increment(x, value=1.0, name=None):
......@@ -3905,10 +3713,7 @@ def increment(x, value=1.0, name=None):
"""
if in_dygraph_mode():
return _C_ops.increment_(x, value)
if _in_legacy_dygraph():
return _legacy_C_ops.increment(x, 'step', value)
else:
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'increment'
)
......@@ -3973,13 +3778,8 @@ def all(x, axis=None, keepdim=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.all(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis(axis, x)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_all(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
attrs = {
'dim': axis,
'keep_dim': keepdim,
......@@ -3992,7 +3792,10 @@ def all(x, axis=None, keepdim=False, name=None):
helper = LayerHelper('all', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reduce_all', inputs={'X': x}, outputs={'Out': out}, attrs=attrs
type='reduce_all',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs,
)
return out
......@@ -4049,13 +3852,8 @@ def any(x, axis=None, keepdim=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.any(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis(axis, x)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_any(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
attrs = {
'dim': axis,
'keep_dim': keepdim,
......@@ -4069,7 +3867,10 @@ def any(x, axis=None, keepdim=False, name=None):
helper = LayerHelper('any', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reduce_any', inputs={'X': x}, outputs={'Out': out}, attrs=attrs
type='reduce_any',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs,
)
return out
......@@ -4137,10 +3938,7 @@ def conj(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.conj(x)
if paddle.in_dynamic_mode():
return _legacy_C_ops.conj(x)
else:
check_variable_and_dtype(
x,
"x",
......@@ -4149,7 +3947,9 @@ def conj(x, name=None):
)
helper = LayerHelper('conj', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
helper.append_op(type='conj', inputs={'X': x}, outputs={'Out': [out]})
return out
......@@ -4184,9 +3984,6 @@ def digamma(x, name=None):
if in_dygraph_mode():
return _C_ops.digamma(x)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.digamma(x)
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'digamma')
helper = LayerHelper('digamma', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
......@@ -4221,9 +4018,7 @@ def lgamma(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.lgamma(x)
elif _in_legacy_dygraph():
return _legacy_C_ops.lgamma(x)
else:
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'lgamma')
helper = LayerHelper('lgamma', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
......@@ -4303,9 +4098,6 @@ def atan2(x, y, name=None):
if in_dygraph_mode():
return _C_ops.atan2(x, y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.atan2(x, y)
else:
check_variable_and_dtype(
x,
......@@ -4367,18 +4159,21 @@ def logit(x, eps=None, name=None):
# [-1.0277, -4.5365, -0.9544, -1.3269, 1.4468]
"""
if eps is None:
eps = 0.0
if _in_legacy_dygraph():
return _legacy_C_ops.logit(x, 'eps', eps)
if in_dygraph_mode():
return _C_ops.logit(x, eps)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'logit')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'logit'
)
helper = LayerHelper("logit", **locals())
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='logit', inputs={'X': x}, outputs={'Out': out}, attrs={'eps': eps}
type='logit',
inputs={'X': x},
outputs={'Out': out},
attrs={'eps': eps},
)
return out
......@@ -4419,17 +4214,15 @@ def lerp(x, y, weight, name=None):
weight = paddle.to_tensor(weight, dtype=x.dtype)
return _C_ops.lerp(x, y, weight)
if _in_legacy_dygraph():
if isinstance(weight, float):
weight = paddle.to_tensor(weight, dtype=x.dtype)
return _legacy_C_ops.lerp(x, y, weight)
else:
if isinstance(weight, float):
weight = paddle.full(shape=[1], fill_value=weight, dtype=x.dtype)
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'lerp')
check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'lerp')
check_variable_and_dtype(weight, 'weight', ['float32', 'float64'], 'lerp')
check_variable_and_dtype(
weight, 'weight', ['float32', 'float64'], 'lerp'
)
helper = LayerHelper('lerp', **locals())
inputs = {'X': x, 'Y': y, 'Weight': weight}
......@@ -4456,9 +4249,7 @@ def lerp_(x, y, weight, name=None):
out_shape, x.shape
)
)
if in_dygraph_mode():
return _C_ops.lerp_(x, y, weight)
return _legacy_C_ops.lerp_(x, y, weight)
def erfinv(x, name=None):
......@@ -4488,12 +4279,8 @@ def erfinv(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.erfinv(x)
else:
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'erfinv')
if paddle.in_dynamic_mode():
return _legacy_C_ops.erfinv(x)
helper = LayerHelper('erfinv', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='erfinv', inputs={'X': x}, outputs={'Out': out})
......@@ -4507,9 +4294,7 @@ def erfinv_(x, name=None):
Please refer to :ref:`api_tensor_erfinv`.
"""
check_type(x, 'x', (paddle.Tensor, Variable), 'erfinv')
if in_dygraph_mode():
return _C_ops.erfinv_(x)
return _legacy_C_ops.erfinv_(x)
def rad2deg(x, name=None):
......@@ -4558,10 +4343,6 @@ def rad2deg(x, name=None):
if convert_dtype(x.dtype) in ['int32', 'int64']:
x = cast(x, dtype="float32")
return _C_ops.scale(x, rad2deg_scale, 0.0, True)
elif paddle.in_dynamic_mode():
if convert_dtype(x.dtype) in ['int32', 'int64']:
x = cast(x, dtype="float32")
return _legacy_C_ops.scale(x, 'scale', rad2deg_scale)
else:
check_variable_and_dtype(
x, 'x', ['int32', 'int64', 'float32', 'float64'], 'rad2deg'
......@@ -4626,10 +4407,6 @@ def deg2rad(x, name=None):
if convert_dtype(x.dtype) in ['int32', 'int64']:
x = cast(x, dtype="float32")
return _C_ops.scale(x, deg2rad_scale, 0.0, True)
elif paddle.in_dynamic_mode():
if convert_dtype(x.dtype) in ['int32', 'int64']:
x = cast(x, dtype="float32")
return _legacy_C_ops.scale(x, 'scale', deg2rad_scale)
else:
check_variable_and_dtype(
x, 'x', ['int32', 'int64', 'float32', 'float64'], 'deg2rad'
......@@ -4729,7 +4506,7 @@ def gcd(x, y, name=None):
)
return (paddle.where(x < y, y, x), paddle.where(x < y, x, y))
if paddle.in_dynamic_mode():
if in_dygraph_mode():
while _gcd_cond_fn(x, y):
x, y = _gcd_body_fn(x, y)
......@@ -4907,68 +4684,6 @@ def diff(x, n=1, axis=-1, prepend=None, append=None, name=None):
return _C_ops.logical_xor(input_back, input_front)
else:
return _C_ops.subtract(input_back, input_front)
elif _in_legacy_dygraph():
has_pend = False
input_list = []
if prepend is not None and append is not None:
input_list = [prepend, x, append]
has_pend = True
elif prepend is not None:
input_list = [prepend, x]
has_pend = True
elif append is not None:
input_list = [x, append]
has_pend = True
if has_pend:
new_input = _varbase_creator()
_legacy_C_ops.concat(input_list, new_input, 'axis', axis)
else:
new_input = x
attrs_1 = ()
attrs_2 = ()
dim_len = new_input.shape[axis]
starts_1 = [0]
attrs_1 += ('starts', starts_1)
ends_1 = [dim_len - 1]
attrs_1 += ('ends', ends_1)
input_front = _legacy_C_ops.slice(
new_input,
None,
None,
None,
None,
'axes',
axes,
'infer_flags',
infer_flags,
*attrs_1
)
starts_2 = [1]
attrs_2 += ('starts', starts_2)
ends_2 = [dim_len]
attrs_2 += ('ends', ends_2)
input_back = _legacy_C_ops.slice(
new_input,
None,
None,
None,
None,
'axes',
axes,
'infer_flags',
infer_flags,
*attrs_2
)
if x.dtype == paddle.bool:
return _legacy_C_ops.logical_xor(input_back, input_front)
else:
return paddle.tensor.math._subtract_with_axis(
input_back, input_front, axis=axis
)
else:
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'bool', 'int32', 'int64'], 'diff'
......@@ -5082,9 +4797,7 @@ def angle(x, name=None):
if in_dygraph_mode():
return _C_ops.angle(x)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.angle(x)
else:
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'complex64', 'complex128'], 'angle'
)
......@@ -5143,10 +4856,11 @@ def heaviside(x, y, name=None):
op_type = 'elementwise_heaviside'
axis = -1
act = None
if _non_static_mode():
if in_dygraph_mode():
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
return _elementwise_op(LayerHelper(op_type, **locals()))
......@@ -5191,12 +4905,6 @@ def frac(x, name=None):
if in_dygraph_mode():
y = _C_ops.trunc(x)
return _C_ops.subtract(x, y)
else:
if _in_legacy_dygraph():
y = _legacy_C_ops.trunc(x)
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type
)
else:
inputs = {"X": x}
attrs = {}
......@@ -5334,7 +5042,7 @@ def take(x, index, mode='raise', name=None):
)
)
if paddle.in_dynamic_mode():
if in_dygraph_mode():
if not isinstance(index, (paddle.Tensor, Variable)):
raise TypeError(
"The type of 'index' must be Tensor, but got {}".format(
......
python/paddle/tensor/ops.py
浏览文件 @ 861fef52
......@@ -12,9 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .. import _C_ops, _legacy_C_ops
from .. import _C_ops
from ..fluid.data_feeder import check_variable_and_dtype
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode
from ..fluid.framework import in_dygraph_mode
from ..framework import LayerHelper
from .layer_function_generator import (
add_sample_code,
......@@ -218,10 +218,10 @@ def acos(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.acos(x)
if _in_legacy_dygraph():
return _legacy_C_ops.acos(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'acos')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'acos'
)
helper = LayerHelper('acos', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='acos', inputs={"X": x}, outputs={"Out": out})
......@@ -255,10 +255,10 @@ def acosh(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.acosh(x)
if _in_legacy_dygraph():
return _legacy_C_ops.acosh(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'acosh')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'acosh'
)
helper = LayerHelper('acosh', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='acosh', inputs={"X": x}, outputs={"Out": out})
......@@ -292,10 +292,10 @@ def asin(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.asin(x)
if _in_legacy_dygraph():
return _legacy_C_ops.asin(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'asin')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'asin'
)
helper = LayerHelper('asin', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='asin', inputs={"X": x}, outputs={"Out": out})
......@@ -329,10 +329,10 @@ def asinh(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.asinh(x)
if _in_legacy_dygraph():
return _legacy_C_ops.asinh(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'asinh')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'asinh'
)
helper = LayerHelper('asinh', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='asinh', inputs={"X": x}, outputs={"Out": out})
......@@ -366,10 +366,10 @@ def atan(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.atan(x)
if _in_legacy_dygraph():
return _legacy_C_ops.atan(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'atan')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'atan'
)
helper = LayerHelper('atan', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='atan', inputs={"X": x}, outputs={"Out": out})
......@@ -403,10 +403,10 @@ def atanh(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.atanh(x)
if _in_legacy_dygraph():
return _legacy_C_ops.atanh(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'atanh')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'atanh'
)
helper = LayerHelper('atanh', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='atanh', inputs={"X": x}, outputs={"Out": out})
......@@ -441,10 +441,10 @@ def ceil(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.ceil(x)
if _in_legacy_dygraph():
return _legacy_C_ops.ceil(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'ceil')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'ceil'
)
helper = LayerHelper('ceil', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='ceil', inputs={"X": x}, outputs={"Out": out})
......@@ -480,10 +480,10 @@ def cos(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.cos(x)
if _in_legacy_dygraph():
return _legacy_C_ops.cos(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'cos')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'cos'
)
helper = LayerHelper('cos', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='cos', inputs={"X": x}, outputs={"Out": out})
......@@ -519,10 +519,10 @@ def cosh(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.cosh(x)
if _in_legacy_dygraph():
return _legacy_C_ops.cosh(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'cosh')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'cosh'
)
helper = LayerHelper('cosh', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='cosh', inputs={"X": x}, outputs={"Out": out})
......@@ -557,9 +557,7 @@ def exp(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.exp(x)
if _in_legacy_dygraph():
return _legacy_C_ops.exp(x)
else:
check_variable_and_dtype(
x,
'x',
......@@ -608,10 +606,10 @@ def expm1(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.expm1(x)
if _in_legacy_dygraph():
return _legacy_C_ops.expm1(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'expm1')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'expm1'
)
helper = LayerHelper('expm1', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='expm1', inputs={"X": x}, outputs={"Out": out})
......@@ -646,10 +644,10 @@ def floor(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.floor(x)
if _in_legacy_dygraph():
return _legacy_C_ops.floor(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'floor')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'floor'
)
helper = LayerHelper('floor', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='floor', inputs={"X": x}, outputs={"Out": out})
......@@ -684,15 +682,15 @@ def reciprocal(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.reciprocal(x)
if _in_legacy_dygraph():
return _legacy_C_ops.reciprocal(x)
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'reciprocal'
)
helper = LayerHelper('reciprocal', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='reciprocal', inputs={"X": x}, outputs={"Out": out})
helper.append_op(
type='reciprocal', inputs={"X": x}, outputs={"Out": out}
)
return out
......@@ -731,10 +729,10 @@ def round(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.round(x)
if _in_legacy_dygraph():
return _legacy_C_ops.round(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'round')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'round'
)
helper = LayerHelper('round', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='round', inputs={"X": x}, outputs={"Out": out})
......@@ -770,10 +768,10 @@ def rsqrt(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.rsqrt(x)
if _in_legacy_dygraph():
return _legacy_C_ops.rsqrt(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'rsqrt')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'rsqrt'
)
helper = LayerHelper('rsqrt', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='rsqrt', inputs={"X": x}, outputs={"Out": out})
......@@ -808,9 +806,7 @@ def sigmoid(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.sigmoid(x)
if _in_legacy_dygraph():
return _legacy_C_ops.sigmoid(x)
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'sigmoid'
)
......@@ -847,10 +843,10 @@ def sin(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.sin(x)
if _in_legacy_dygraph():
return _legacy_C_ops.sin(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'sin')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'sin'
)
helper = LayerHelper('sin', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='sin', inputs={"X": x}, outputs={"Out": out})
......@@ -884,10 +880,10 @@ def sinh(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.sinh(x)
if _in_legacy_dygraph():
return _legacy_C_ops.sinh(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'sinh')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'sinh'
)
helper = LayerHelper('sinh', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='sinh', inputs={"X": x}, outputs={"Out": out})
......@@ -920,10 +916,10 @@ def sqrt(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.sqrt(x)
if _in_legacy_dygraph():
return _legacy_C_ops.sqrt(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'sqrt')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'sqrt'
)
helper = LayerHelper('sqrt', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='sqrt', inputs={"X": x}, outputs={"Out": out})
......@@ -956,9 +952,7 @@ def square(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.square(x)
if _in_legacy_dygraph():
return _legacy_C_ops.square(x)
else:
check_variable_and_dtype(
x,
'x',
......@@ -1008,10 +1002,10 @@ def tan(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.tan(x)
if _in_legacy_dygraph():
return _legacy_C_ops.tan(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'tan')
else:
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'tan'
)
helper = LayerHelper('tan', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='tan', inputs={"X": x}, outputs={"Out": out})
......
python/paddle/tensor/random.py
浏览文件 @ 861fef52
......@@ -16,11 +16,7 @@
import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import (
_current_expected_place,
_in_legacy_dygraph,
in_dygraph_mode,
)
from paddle.fluid.framework import _current_expected_place, in_dygraph_mode
from paddle.static import Variable
from ..fluid.data_feeder import (
......@@ -80,10 +76,7 @@ def bernoulli(x, name=None):
if in_dygraph_mode():
return _C_ops.bernoulli(x)
if _in_legacy_dygraph():
return _legacy_C_ops.bernoulli(x)
else:
check_variable_and_dtype(x, "x", ["float32", "float64"], "bernoulli")
helper = LayerHelper("randint", **locals())
......@@ -129,10 +122,7 @@ def poisson(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.poisson(x)
if paddle.in_dynamic_mode():
return _legacy_C_ops.poisson(x)
else:
check_variable_and_dtype(x, "x", ["float32", "float64"], "poisson")
helper = LayerHelper("poisson", **locals())
......@@ -197,12 +187,7 @@ def multinomial(x, num_samples=1, replacement=False, name=None):
if in_dygraph_mode():
return _C_ops.multinomial(x, num_samples, replacement)
if _in_legacy_dygraph():
return _legacy_C_ops.multinomial(
x, 'num_samples', num_samples, 'replacement', replacement
)
else:
check_variable_and_dtype(x, "x", ["float32", "float64"], "multinomial")
helper = LayerHelper("multinomial", **locals())
......@@ -356,22 +341,7 @@ def gaussian(shape, mean=0.0, std=1.0, seed=0, dtype=None, name=None):
return _C_ops.gaussian(
shape, float(mean), float(std), seed, dtype, place
)
if _in_legacy_dygraph():
shape = utils.convert_shape_to_list(shape)
return _legacy_C_ops.gaussian_random(
'shape',
shape,
'mean',
float(mean),
'std',
float(std),
'seed',
seed,
'dtype',
dtype,
)
else:
check_shape(shape, op_type_for_check)
check_dtype(dtype, 'dtype', ['float32', 'float64'], op_type_for_check)
......@@ -390,7 +360,10 @@ def gaussian(shape, mean=0.0, std=1.0, seed=0, dtype=None, name=None):
helper = LayerHelper('gaussian', **locals())
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='gaussian_random', inputs=inputs, outputs={'Out': out}, attrs=attrs
type='gaussian_random',
inputs=inputs,
outputs={'Out': out},
attrs=attrs,
)
out.stop_gradient = True
return out
......@@ -550,7 +523,7 @@ def normal(mean=0.0, std=1.0, shape=None, name=None):
# [1.00780561 3.78457445 5.81058198] # random
"""
if not paddle.in_dynamic_mode():
if not in_dygraph_mode():
check_type(mean, 'mean', (int, float, Variable), 'normal')
check_type(std, 'std', (int, float, Variable), 'normal')
if isinstance(mean, Variable):
......@@ -588,7 +561,7 @@ def normal(mean=0.0, std=1.0, shape=None, name=None):
return gaussian(shape=shape, mean=mean, std=std, name=name)
out = out * std + mean
if not paddle.in_dynamic_mode():
if not in_dygraph_mode():
out.stop_grediant = True
return out
......@@ -680,22 +653,7 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
seed,
_current_expected_place(),
)
if _in_legacy_dygraph():
shape = utils.convert_shape_to_list(shape)
return _legacy_C_ops.uniform_random(
'shape',
shape,
'min',
float(min),
'max',
float(max),
'seed',
seed,
'dtype',
dtype,
)
else:
check_type(shape, 'shape', (list, tuple, Variable), 'uniform/rand')
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform/rand')
check_type(min, 'min', (float, int, Variable), 'uniform/rand')
......@@ -710,7 +668,10 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
helper = LayerHelper("uniform", **locals())
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="uniform_random", inputs=inputs, attrs=attrs, outputs={"Out": out}
type="uniform_random",
inputs=inputs,
attrs=attrs,
outputs={"Out": out},
)
out.stop_gradient = True
return out
......@@ -751,12 +712,7 @@ def uniform_(x, min=-1.0, max=1.0, seed=0, name=None):
# [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random
# [ 0.433519, 0.39483607, -0.8660099, 0.83664286]] # random
"""
if in_dygraph_mode():
return _C_ops.uniform_inplace_(x, min, max, seed, 0, 0, 1.0)
else:
return _legacy_C_ops.uniform_random_inplace_(
x, 'min', min, 'max', max, 'seed', seed
)
def randint(low=0, high=None, shape=[1], dtype=None, name=None):
......@@ -841,12 +797,7 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
shape = utils.convert_shape_to_list(shape)
place = _current_expected_place()
return _C_ops.randint(low, high, shape, dtype, place)
if _in_legacy_dygraph():
shape = utils.convert_shape_to_list(shape)
return _legacy_C_ops.randint(
'shape', shape, 'low', low, 'high', high, 'seed', 0, 'dtype', dtype
)
else:
check_shape(shape, 'randint')
check_dtype(dtype, 'dtype', ['int32', 'int64'], 'randint')
if low >= high:
......@@ -1015,7 +966,7 @@ def randint_like(x, low=0, high=None, dtype=None, name=None):
"high = {1}".format(low, high)
)
if paddle.in_dynamic_mode():
if in_dygraph_mode():
shape = utils.convert_shape_to_list(shape)
out = _legacy_C_ops.randint(
'shape',
......@@ -1031,7 +982,7 @@ def randint_like(x, low=0, high=None, dtype=None, name=None):
)
out = paddle.cast(out, dtype)
return out
else:
check_shape(shape, 'randint_like')
check_dtype(
dtype,
......@@ -1095,11 +1046,11 @@ def randperm(n, dtype="int64", name=None):
if in_dygraph_mode():
return _C_ops.randperm(n, dtype, _current_expected_place())
if _in_legacy_dygraph():
return _legacy_C_ops.randperm('n', n, 'seed', 0, 'dtype', dtype)
else:
if n < 1:
raise ValueError("The input n should be greater than 0 in randperm op.")
raise ValueError(
"The input n should be greater than 0 in randperm op."
)
check_dtype(
dtype, 'dtype', ['int64', 'int32', 'float32', 'float64'], 'randperm'
)
......@@ -1199,9 +1150,7 @@ def exponential_(x, lam=1.0, name=None):
"""
if in_dygraph_mode():
return _C_ops.exponential_(x, lam)
elif paddle.in_dynamic_mode():
return _legacy_C_ops.exponential_(x, "lambda", lam)
else:
check_variable_and_dtype(x, "x", ["float32", "float64"], "exponential")
helper = LayerHelper("exponential", **locals())
......
python/paddle/tensor/search.py
浏览文件 @ 861fef52
......@@ -17,14 +17,12 @@
import numpy as np
import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle import _C_ops
from paddle.common_ops_import import VarDesc, Variable
from ..fluid.data_feeder import check_dtype, check_variable_and_dtype
from ..fluid.framework import _in_legacy_dygraph
from ..framework import (
LayerHelper,
_non_static_mode,
convert_np_dtype_to_dtype_,
core,
in_dygraph_mode,
......@@ -99,12 +97,7 @@ def argsort(x, axis=-1, descending=False, name=None):
if in_dygraph_mode():
_, ids = _C_ops.argsort(x, axis, descending)
return ids
if _in_legacy_dygraph():
_, ids = _legacy_C_ops.argsort(
x, 'axis', axis, 'descending', descending
)
return ids
else:
check_variable_and_dtype(
x,
'x',
......@@ -187,20 +180,7 @@ def argmax(x, axis=None, keepdim=False, dtype="int64", name=None):
if in_dygraph_mode():
return _C_ops.argmax(x, axis, keepdim, flatten, var_dtype)
if _in_legacy_dygraph():
out = _legacy_C_ops.arg_max(
x,
'axis',
axis,
'dtype',
var_dtype,
'keepdims',
keepdim,
'flatten',
flatten,
)
return out
else:
helper = LayerHelper("argmax", **locals())
check_variable_and_dtype(
x,
......@@ -281,20 +261,7 @@ def argmin(x, axis=None, keepdim=False, dtype="int64", name=None):
if in_dygraph_mode():
return _C_ops.argmin(x, axis, keepdim, flatten, var_dtype)
if _in_legacy_dygraph():
out = _legacy_C_ops.arg_min(
x,
'axis',
axis,
'dtype',
var_dtype,
'keepdims',
keepdim,
'flatten',
flatten,
)
return out
else:
helper = LayerHelper("argmin", **locals())
check_variable_and_dtype(
x,
......@@ -354,10 +321,7 @@ def index_select(x, index, axis=0, name=None):
if in_dygraph_mode():
return _C_ops.index_select(x, index, axis)
if _in_legacy_dygraph():
return _legacy_C_ops.index_select(x, index, 'dim', axis)
else:
helper = LayerHelper("index_select", **locals())
check_variable_and_dtype(
x,
......@@ -366,7 +330,10 @@ def index_select(x, index, axis=0, name=None):
'paddle.tensor.search.index_select',
)
check_variable_and_dtype(
index, 'index', ['int32', 'int64'], 'paddle.tensor.search.index_select'
index,
'index',
['int32', 'int64'],
'paddle.tensor.search.index_select',
)
out = helper.create_variable_for_type_inference(x.dtype)
......@@ -438,8 +405,6 @@ def nonzero(x, as_tuple=False):
if in_dygraph_mode():
outs = _C_ops.nonzero(x)
elif paddle.in_dynamic_mode():
outs = _legacy_C_ops.where_index(x)
else:
helper = LayerHelper("where_index", **locals())
......@@ -522,12 +487,7 @@ def sort(x, axis=-1, descending=False, name=None):
if in_dygraph_mode():
outs, _ = _C_ops.argsort(x, axis, descending)
return outs
if _in_legacy_dygraph():
outs, _ = _legacy_C_ops.argsort(
x, 'axis', axis, 'descending', descending
)
return outs
else:
helper = LayerHelper("sort", **locals())
out = helper.create_variable_for_type_inference(
dtype=x.dtype, stop_gradient=False
......@@ -577,9 +537,7 @@ def mode(x, axis=-1, keepdim=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.mode(x, axis, keepdim)
if _in_legacy_dygraph():
return _legacy_C_ops.mode(x, "axis", axis, "keepdim", keepdim)
else:
helper = LayerHelper("mode", **locals())
inputs = {"X": [x]}
attrs = {}
......@@ -687,11 +645,6 @@ def where(condition, x=None, y=None, name=None):
if in_dygraph_mode():
return _C_ops.where(broadcast_condition, broadcast_x, broadcast_y)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.where(
broadcast_condition, broadcast_x, broadcast_y
)
else:
helper = LayerHelper("where", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -784,9 +737,6 @@ def index_sample(x, index):
"""
if in_dygraph_mode():
return _C_ops.index_sample(x, index)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.index_sample(x, index)
else:
helper = LayerHelper("index_sample", **locals())
check_variable_and_dtype(
......@@ -843,9 +793,7 @@ def masked_select(x, mask, name=None):
if in_dygraph_mode():
return _C_ops.masked_select(x, mask)
if _in_legacy_dygraph():
return _legacy_C_ops.masked_select(x, mask)
else:
helper = LayerHelper("masked_select", **locals())
check_variable_and_dtype(
x,
......@@ -858,7 +806,9 @@ def masked_select(x, mask, name=None):
)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='masked_select', inputs={'X': x, 'Mask': mask}, outputs={'Y': out}
type='masked_select',
inputs={'X': x, 'Mask': mask},
outputs={'Y': out},
)
return out
......@@ -916,26 +866,7 @@ def topk(x, k, axis=None, largest=True, sorted=True, name=None):
axis = -1
out, indices = _C_ops.topk(x, k, axis, largest, sorted)
return out, indices
if _non_static_mode():
if axis is None:
out, indices = _legacy_C_ops.top_k_v2(
x, 'k', int(k), 'largest', largest, 'sorted', sorted
)
else:
out, indices = _legacy_C_ops.top_k_v2(
x,
'k',
int(k),
'axis',
axis,
'largest',
largest,
'sorted',
sorted,
)
return out, indices
helper = LayerHelper("top_k_v2", **locals())
inputs = {"X": [x]}
attrs = {}
......@@ -1065,12 +996,7 @@ def searchsorted(
"""
if in_dygraph_mode():
return _C_ops.searchsorted(sorted_sequence, values, out_int32, right)
if _in_legacy_dygraph():
return _legacy_C_ops.searchsorted(
sorted_sequence, values, "out_int32", out_int32, "right", right
)
else:
check_variable_and_dtype(
sorted_sequence,
'SortedSequence',
......@@ -1135,16 +1061,10 @@ def kthvalue(x, k, axis=None, keepdim=False, name=None):
# [[0, 2],
# [1, 2]]))
"""
if _non_static_mode():
if in_dygraph_mode():
if axis is not None:
if _in_legacy_dygraph():
return _legacy_C_ops.kthvalue(
x, 'k', k, "axis", axis, "keepdim", keepdim
)
return _C_ops.kthvalue(x, k, axis, keepdim)
else:
if _in_legacy_dygraph():
return _legacy_C_ops.kthvalue(x, 'k', k, "keepdim", keepdim)
return _C_ops.kthvalue(x, k, -1, keepdim)
helper = LayerHelper("kthvalue", **locals())
......
python/paddle/tensor/stat.py
浏览文件 @ 861fef52
......@@ -16,7 +16,7 @@
import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import _in_legacy_dygraph, in_dygraph_mode
from paddle.fluid.framework import in_dygraph_mode
from ..fluid.data_feeder import check_type, check_variable_and_dtype
from ..framework import LayerHelper, core
......@@ -81,13 +81,8 @@ def mean(x, axis=None, keepdim=False, name=None):
"""
if in_dygraph_mode():
return _C_ops.mean(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis_with_tensor(axis, x)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_mean(
x, 'dim', axis, 'keep_dim', keepdim, 'reduce_all', reduce_all
)
check_variable_and_dtype(
x,
'x/input',
......@@ -111,7 +106,10 @@ def mean(x, axis=None, keepdim=False, name=None):
attrs = {'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all}
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='reduce_mean', inputs={'X': x}, outputs={'Out': out}, attrs=attrs
type='reduce_mean',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs,
)
return out
......@@ -146,7 +144,7 @@ def var(x, axis=None, unbiased=True, keepdim=False, name=None):
out2 = paddle.var(x, axis=1)
# [1. 4.33333333]
"""
if not paddle.in_dynamic_mode():
if not in_dygraph_mode():
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'var')
u = mean(x, axis, True, name)
......@@ -211,7 +209,7 @@ def std(x, axis=None, unbiased=True, keepdim=False, name=None):
# [1. 2.081666]
"""
if not paddle.in_dynamic_mode():
if not in_dygraph_mode():
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'std')
out = var(**locals())
......@@ -243,9 +241,7 @@ def numel(x, name=None):
"""
if in_dygraph_mode():
return _C_ops.numel(x)
elif _in_legacy_dygraph():
return _legacy_C_ops.size(x)
else:
if not isinstance(x, Variable):
raise TypeError("x must be a Tensor in numel")
helper = LayerHelper('numel', **locals())
......@@ -331,14 +327,17 @@ def nanmedian(x, axis=None, keepdim=True, name=None):
if len(axis) != len(set(axis)):
raise ValueError("Axis has duplicated elements.")
if _in_legacy_dygraph():
if in_dygraph_mode():
median_index, out = _legacy_C_ops.nanmedian(
x, 'axis', axis, 'keepdim', keepdim
)
return out
else:
check_variable_and_dtype(
x, 'X', ['int32', 'int64', 'float16', 'float32', 'float64'], 'nanmedian'
x,
'X',
['int32', 'int64', 'float16', 'float32', 'float64'],
'nanmedian',
)
helper = LayerHelper('nanmedian', **locals())
......@@ -534,7 +533,7 @@ def _compute_quantile(x, q, axis=None, keepdim=False, ignore_nan=False):
for q_num in q:
if q_num < 0 or q_num > 1:
raise ValueError("q should be in range [0, 1]")
if paddle.in_dynamic_mode():
if in_dygraph_mode():
q_num = paddle.to_tensor(q_num, dtype='float64')
if ignore_nan:
indices.append(q_num * (valid_counts - 1))
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或