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未验证 提交 6de463d3 编写于 作者: Z Zhou Wei 提交者： GitHub
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expose and unify the Tensor concepts to the user (#25978) 

* expose and unify the Tensor concepts to the user

* expose tensor to user

* add copy place for Tensor

* add copy place for Tensor

* add note

* add macro PADDLE_WITH_CUDA

* remove RUN_TYPE=DIST

* fix some error
上级 fbd4d3cc
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Showing with 401 addition and 79 deletion
paddle/fluid/framework/tensor_util.cc
浏览文件 @ 6de463d3
......@@ -60,6 +60,10 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
size);
} else if (platform::is_cpu_place(src_place) && // NOLINT
platform::is_cuda_pinned_place(dst_place)) {
memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
} else if (platform::is_gpu_place(src_place) && // NOLINT
platform::is_cpu_place(dst_place)) {
auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
......@@ -82,6 +86,28 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
} else if (platform::is_gpu_place(src_place) && // NOLINT
platform::is_cuda_pinned_place(dst_place)) {
auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
auto dst_cuda_pinned_place =
BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place);
auto ctx_place = ctx.GetPlace();
PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx_place), true,
platform::errors::PreconditionNotMet(
"Device context place mismatch. When copying Tensor "
"data from GPU memory to CUDA Pinned memory, current "
"device context place should be GPU."));
auto ctx_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, ctx_place);
PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place,
platform::errors::PreconditionNotMet(
"The source GPU device and current device context do "
"not match. The source GPU device number is %d, but "
"device context GPU number is %d.",
src_gpu_place.device, ctx_gpu_place.device));
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
memory::Copy(dst_cuda_pinned_place, dst_ptr, src_gpu_place, src_ptr, size,
stream);
} else if (platform::is_cuda_pinned_place(src_place) &&
platform::is_gpu_place(dst_place)) {
auto src_cuda_pinned_place =
......@@ -180,6 +206,15 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
size);
} else if (platform::is_cpu_place(src_place) && // NOLINT
platform::is_cuda_pinned_place(dst_place)) {
memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size);
} else if (platform::is_gpu_place(src_place) && // NOLINT
platform::is_cuda_pinned_place(dst_place)) {
memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::CUDAPlace, src_place), src_ptr, size,
nullptr);
} else if (platform::is_gpu_place(src_place) && // NOLINT
platform::is_cpu_place(dst_place)) {
auto src_gpu_place = BOOST_GET_CONST(platform::CUDAPlace, src_place);
......
paddle/fluid/pybind/imperative.cc
浏览文件 @ 6de463d3
......@@ -78,11 +78,15 @@ static void InitTensorForVarBase(imperative::VarBase *self,
const py::array &array,
const platform::Place place,
bool persistable = false,
bool zero_copy = false,
std::string name = "") {
bool zero_copy = false, std::string name = "",
int stop_gradient = -1) {
if (name == "") {
name = imperative::GetCurrentTracer()->GenerateUniqueName("generated_var");
name =
imperative::GetCurrentTracer()->GenerateUniqueName("generated_tensor");
}
VLOG(5) << "Init Tensor as: / name: " << name
<< " / persistable: " << persistable << " / zero_copy: " << zero_copy
<< " / stop_gradient: " << stop_gradient;
new (self) imperative::VarBase(name);
auto *tensor = self->MutableVar()->GetMutable<framework::LoDTensor>();
if (platform::is_cpu_place(place)) {
......@@ -99,6 +103,9 @@ static void InitTensorForVarBase(imperative::VarBase *self,
PADDLE_THROW(platform::errors::InvalidArgument(
"Place should be one of CPUPlace/CUDAPlace/CUDAPinnedPlace"));
}
if (stop_gradient != -1) {
self->SetOverridedStopGradient(stop_gradient);
}
self->SetPersistable(persistable);
self->SetType(framework::proto::VarType::LOD_TENSOR);
self->SetDataType(tensor->type());
......@@ -106,12 +113,11 @@ static void InitTensorForVarBase(imperative::VarBase *self,
static void InitVarBaseFromNumpyWithKwargs(imperative::VarBase *self,
const py::kwargs &kwargs) {
VLOG(4) << "Init VarBase";
VLOG(4) << "Init VarBase from kwargs: ";
PADDLE_ENFORCE_EQ(
kwargs.contains("value"), true,
platform::errors::NotFound(
"The kwargs used to create Varbase misses argument: value"));
auto persistable = kwargs.contains("persistable")
? kwargs["persistable"].cast<bool>()
: false;
......@@ -120,10 +126,14 @@ static void InitVarBaseFromNumpyWithKwargs(imperative::VarBase *self,
auto zero_copy =
kwargs.contains("zero_copy") ? kwargs["zero_copy"].cast<bool>() : false;
auto name = kwargs.contains("name") ? kwargs["name"].cast<std::string>() : "";
auto stop_gradient = kwargs.contains("stop_gradient")
? kwargs["stop_gradient"].cast<int>()
: -1;
auto default_place = imperative::GetCurrentTracer()->ExpectedPlace();
auto place = kwargs.contains("place") ? PyObjectToPlace(kwargs["place"])
: default_place;
InitTensorForVarBase(self, array, place, persistable, zero_copy, name);
InitTensorForVarBase(self, array, place, persistable, zero_copy, name,
stop_gradient);
}
template <typename P>
......@@ -131,15 +141,24 @@ static void InitVarBaseFromNumpyWithArg(imperative::VarBase *self,
const py::array &array, const P &place,
bool persistable = false,
bool zero_copy = false,
std::string name = "") {
VLOG(4) << "Init VarBase";
// 0: self, 1: value, 2: place, 3: persistable, 4: zero_copy, 5: name
std::string name = "",
int stop_gradient = -1) {
VLOG(4) << "Init VarBase from Arg: ";
// 0: self, 1: value, 2: place, 3: persistable, 4: zero_copy, 5: name , 6:
// stop_gradient
if (name == "") {
name = imperative::GetCurrentTracer()->GenerateUniqueName("generated_var");
name =
imperative::GetCurrentTracer()->GenerateUniqueName("generated_tensor");
}
VLOG(5) << "Init Tensor as: / name: " << name
<< " / persistable: " << persistable << " / zero_copy: " << zero_copy
<< " / stop_gradient: " << stop_gradient;
new (self) imperative::VarBase(name);
self->SetPersistable(persistable);
auto *tensor = self->MutableVar()->GetMutable<framework::LoDTensor>();
if (stop_gradient != -1) {
self->SetOverridedStopGradient(stop_gradient);
}
SetTensorFromPyArray<P>(tensor, array, place, zero_copy);
self->SetType(framework::proto::VarType::LOD_TENSOR);
self->SetDataType(tensor->type());
......@@ -147,7 +166,7 @@ static void InitVarBaseFromNumpyWithArg(imperative::VarBase *self,
static void InitVarBaseFromNumpyWithArgDefault(imperative::VarBase *self,
const py::array &array) {
VLOG(4) << "Init VarBase";
VLOG(4) << "Init VarBase from numpy: ";
auto place = imperative::GetCurrentTracer()->ExpectedPlace();
InitTensorForVarBase(self, array, place);
}
......@@ -157,7 +176,7 @@ static void InitVarBaseFromTensorWithArgDefault(
VLOG(4) << "Init VarBase";
auto place = imperative::GetCurrentTracer()->ExpectedPlace();
new (self) imperative::VarBase(
imperative::GetCurrentTracer()->GenerateUniqueName("generated_var"));
imperative::GetCurrentTracer()->GenerateUniqueName("generated_tensor"));
self->SetPersistable(false);
self->SetType(framework::proto::VarType::LOD_TENSOR);
self->SetDataType(tensor.type());
......@@ -551,7 +570,7 @@ void BindImperative(py::module *m_ptr) {
std::string act_name = "";
if (!name.ptr() || name.ptr() == Py_None) {
act_name = imperative::GetCurrentTracer()->GenerateUniqueName(
"generated_var");
"generated_tensor");
} else {
act_name = name.cast<std::string>();
}
......@@ -567,13 +586,16 @@ void BindImperative(py::module *m_ptr) {
})
.def("__init__", &InitVarBaseFromNumpyWithArg<platform::CPUPlace>,
py::arg("value"), py::arg("place"), py::arg("persistable") = false,
py::arg("zero_copy") = false, py::arg("name") = "")
py::arg("zero_copy") = false, py::arg("name") = "",
py::arg("stop_gradient") = -1)
.def("__init__", &InitVarBaseFromNumpyWithArg<platform::CUDAPlace>,
py::arg("value"), py::arg("place"), py::arg("persistable") = false,
py::arg("zero_copy") = false, py::arg("name") = "")
py::arg("zero_copy") = false, py::arg("name") = "",
py::arg("stop_gradient") = -1)
.def("__init__", &InitVarBaseFromNumpyWithArg<platform::CUDAPinnedPlace>,
py::arg("value"), py::arg("place"), py::arg("persistable") = false,
py::arg("zero_copy") = false, py::arg("name") = "")
py::arg("zero_copy") = false, py::arg("name") = "",
py::arg("stop_gradient") = -1)
.def("__init__", &InitVarBaseFromNumpyWithArgDefault, py::arg("value"))
.def("__init__", &InitVarBaseFromTensorWithArgDefault, py::arg("tensor"))
.def("__init__", &InitVarBaseFromNumpyWithKwargs)
......@@ -796,6 +818,11 @@ void BindImperative(py::module *m_ptr) {
[](const imperative::VarBase &self, const platform::CPUPlace &place,
bool blocking) { return self.NewVarBase(place, blocking); },
py::return_value_policy::copy)
.def("_copy_to",
[](const imperative::VarBase &self,
const platform::CUDAPinnedPlace &place,
bool blocking) { return self.NewVarBase(place, blocking); },
py::return_value_policy::copy)
.def("_copy_to",
[](const imperative::VarBase &self, const platform::CUDAPlace &place,
bool blocking) { return self.NewVarBase(place, blocking); },
......@@ -824,6 +851,9 @@ void BindImperative(py::module *m_ptr) {
return std::vector<int>();
}
})
.def_property_readonly(
"place", [](imperative::VarBase &self) { return self.Place(); },
py::return_value_policy::copy)
.def_property_readonly("type", &imperative::VarBase::Type)
.def_property_readonly("dtype", &imperative::VarBase::DataType);
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 6de463d3
......@@ -1070,7 +1070,7 @@ All parameter, weight, gradient are variables in Paddle.
.def("find_var", &Scope::FindVar, py::arg("name"),
R"DOC(
Find variable named :code:`name` in the current scope or
its parent scope. Return None if not found.
its parent scope. Return None if not found.
Args:
name (str): the variable name.
......@@ -1319,12 +1319,16 @@ All parameter, weight, gradient are variables in Paddle.
std::exit(-1);
#endif
})
#ifdef PADDLE_WITH_CUDA
.def("_type", &PlaceIndex<platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::Place>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CPUPlace>)
.def("_equals",
&IsSamePlace<platform::CUDAPlace, platform::CUDAPinnedPlace>)
.def("_get_device_id",
[](platform::CUDAPlace &self) -> int { return self.GetDeviceId(); })
#endif
.def("__str__", string::to_string<const platform::CUDAPlace &>);
py::class_<paddle::platform::CPUPlace>(m, "CPUPlace", R"DOC(
......
paddle/fluid/pybind/tensor_py.h
浏览文件 @ 6de463d3
......@@ -211,7 +211,7 @@ void SetTensorFromPyArrayT(
}
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Cannot use CUDAPlace in CPU only version, "
"Cannot use CUDAPlace or CUDAPinnedPlace in CPU only version, "
"Please recompile or reinstall Paddle with CUDA support."));
#endif
}
......
python/paddle/__init__.py
浏览文件 @ 6de463d3
......@@ -31,13 +31,15 @@ import paddle.reader
import paddle.dataset
import paddle.batch
batch = batch.batch
import paddle.framework
from .framework import VarBase as Tensor
from .framework import ComplexVariable as ComplexTensor
import paddle.compat
import paddle.distributed
import paddle.sysconfig
import paddle.tensor
import paddle.nn
import paddle.distributed.fleet
import paddle.framework
import paddle.optimizer
import paddle.metric
import paddle.incubate.complex as complex
......@@ -48,9 +50,7 @@ from .tensor.random import randperm
from .tensor.attribute import rank #DEFINE_ALIAS
from .tensor.attribute import shape #DEFINE_ALIAS
from .tensor.creation import create_tensor #DEFINE_ALIAS
# from .tensor.creation import create_lod_tensor #DEFINE_ALIAS
# from .tensor.creation import create_random_int_lodtensor #DEFINE_ALIAS
from .tensor.creation import to_tensor #DEFINE_ALIAS
from .tensor.creation import crop_tensor #DEFINE_ALIAS
from .tensor.creation import diag #DEFINE_ALIAS
from .tensor.creation import eye #DEFINE_ALIAS
......@@ -231,7 +231,6 @@ from .tensor.stat import reduce_mean #DEFINE_ALIAS
from .tensor.stat import std #DEFINE_ALIAS
from .tensor.stat import var #DEFINE_ALIAS
from .fluid.data import data
# from .tensor.tensor import Tensor #DEFINE_ALIAS
# from .tensor.tensor import LoDTensor #DEFINE_ALIAS
# from .tensor.tensor import LoDTensorArray #DEFINE_ALIAS
......
python/paddle/fluid/data_feeder.py
浏览文件 @ 6de463d3
......@@ -50,14 +50,15 @@ def convert_dtype(dtype):
elif isinstance(dtype, type):
if dtype in [
np.bool, np.float16, np.float32, np.float64, np.int8, np.int16,
np.int32, np.int64, np.uint8
np.int32, np.int64, np.uint8, np.complex64, np.complex128
]:
return dtype.__name__
else:
if dtype in [
'bool', 'float16', 'float32', 'float64', 'int8', 'int16',
'int32', 'int64', 'uint8', u'bool', u'float16', u'float32',
u'float64', u'int8', u'int16', u'int32', u'int64', u'uint8'
'int32', 'int64', 'uint8', 'complex64', 'complex128', u'bool',
u'float16', u'float32', u'float64', u'int8', u'int16', u'int32',
u'int64', u'uint8', u'complex64', u'complex128'
]:
# this code is a little bit dangerous, since error could happen
# when casting no-ascii code to str in python2.
......@@ -68,7 +69,7 @@ def convert_dtype(dtype):
raise TypeError(
"dtype must be any of [bool, float16, float32, float64, int8, int16, "
"int32, int64, uint8], but received %s" % dtype)
"int32, int64, uint8, complex64, complex128], but received %s" % dtype)
def check_variable_and_dtype(input,
......
python/paddle/fluid/dygraph/varbase_patch_methods.py
浏览文件 @ 6de463d3
......@@ -50,14 +50,19 @@ def monkey_patch_varbase():
static_var = var_base._to_static_var()
"""
# Note: getattr(self, attr, None) will call x.grad=x.gradient(), but gradient() only available in dygraph.
# It will fail. So, for propery in dygraph only, should not let it getattr(self, attr, None).
attr_not_need_keys = ['grad']
if isinstance(self, ParamBase):
attr_kwargs = self.__dict__.copy()
else:
attr_names = [
name for name in dir(self)
if not (inspect.ismethod(getattr(self, name)) or
name.startswith('_'))
]
attr_names = []
for name in dir(self):
if name not in attr_not_need_keys and not (
inspect.ismethod(getattr(self, name)) or
name.startswith('_')):
attr_names.append(name)
attr_kwargs = {name: getattr(self, name) for name in attr_names}
attr_keys = ['block', 'shape', 'dtype', 'type', 'name', 'persistable']
......@@ -216,6 +221,14 @@ def monkey_patch_varbase():
else:
return np.array(new_ivar.value().get_tensor())
@property
def grad(self):
"""
The alias of gradient().
"""
return self.gradient()
def __str__(self):
"""
Convert a VarBase object to a readable string.
......@@ -239,9 +252,9 @@ def monkey_patch_varbase():
"""
tensor = self.value().get_tensor()
if tensor._is_initialized():
return 'Variable: %s\n%s' % (self.name, str(tensor))
return 'Tensor: %s\n%s' % (self.name, str(tensor))
else:
return 'Variable: %s, not initialized' % (self.name)
return 'Tensor: %s, not initialized' % (self.name)
@property
def block(self):
......@@ -260,8 +273,9 @@ def monkey_patch_varbase():
for method_name, method in (
("__bool__", __bool__), ("__nonzero__", __nonzero__),
("_to_static_var", _to_static_var), ("set_value", set_value),
("block", block), ("backward", backward), ("gradient", gradient),
("__str__", __str__)):
("block", block), ("backward", backward), ("grad", grad),
("gradient", gradient), ("__str__", __str__), ("__repr__", __str__),
("__module__", "paddle"), ("__name__", "Tensor")):
setattr(core.VarBase, method_name, method)
# patch math methods for varbase
......
python/paddle/fluid/framework.py
浏览文件 @ 6de463d3
......@@ -1689,34 +1689,40 @@ def get_all_op_protos():
class ComplexVariable(object):
"""
The Variable defined on the complex number domain. It contains two common
real number Variables as its members, :attr:`real` and :attr:`imag`
The ComplexTensor defined on the complex number domain. It contains two common
real number Tensor as its members, :attr:`real` and :attr:`imag`
holding the real part and imaginary part of complex numbers respectively.
**Notes**:
**The constructor of ComplexVariable should not be invoked directly.**
**The constructor of ComplexTensor should not be invoked directly.**
**Only support dygraph mode at present. Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph ComplexVariable with complex number data.**
**Only support dygraph mode at present. Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph ComplexTensor with complex number data.**
Args:
real (Variable): The Variable holding real-part data.
imag (Variable): The Variable holding imaginery-part data.
real (Tensor): The Tensor holding real-part data.
imag (Tensor): The Tensor holding imaginery-part data.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
import numpy as np
a = np.array([1.0+2.0j, 0.2])
with fluid.dygraph.guard():
var = fluid.dygraph.to_variable(a, name="new_var")
print(var.name, var.dtype, var.shape)
# ({'real': u'new_var.real', 'imag': u'new_var.imag'}, 'complex128', [2L])
print(var.numpy())
# [1. +2.j 0.2+0.j]
paddle.enable_imperative()
x = paddle.to_tensor([1.0+2.0j, 0.2])
print(x.name, x.dtype, x.shape)
# ({'real': 'generated_tensor_0.real', 'imag': 'generated_tensor_0.imag'}, 'complex128', [2L])
print(x.numpy())
# [1. +2.j 0.2+0.j]
print(type(x))
# <class 'paddle.ComplexTensor'>
"""
def __new__(cls, *arg, **kwargs):
cls.__module__ = "paddle"
cls.__name__ = "ComplexTensor"
return super(ComplexVariable, cls).__new__(cls)
def __init__(self, real, imag):
assert real.shape == imag.shape, "The real part and imaginary part " \
"of a ComplexVariable should have the same shape!"
......@@ -1763,7 +1769,9 @@ class ComplexVariable(object):
return self.real.numpy() + 1j * self.imag.numpy()
def __str__(self):
return "REAL: " + self.real.__str__() + "IMAG: " + self.imag.__str__()
return "ComplexTensor[real]: %s\n%s\nComplexTensor[imag]: %s\n%s" % (
self.real.name, str(self.real.value().get_tensor()), self.imag.name,
str(self.imag.value().get_tensor()))
__repr__ = __str__
......@@ -5092,12 +5100,13 @@ class Parameter(Variable):
class ParamBase(core.VarBase):
"""
ParamBase is derived from VarBase( Which is the Variable in Dygraph Mode ). A ParamBase is a persistable
VarBase, and will be updated by optimizers after each iteration.
ParamBase is derived from Tensor( Which is the concept in Dygraph Mode).
A ParamBase is a persistable Tensor, and will be updated by optimizers
after each iteration.
The training of a neural network is essentially the updating of
its ParamBase.
Relative to a general Variable, a ParamBase has several its own
Relative to a general Tensor, a ParamBase has several its own
member variables:
Args:
......@@ -5186,11 +5195,8 @@ class ParamBase(core.VarBase):
# - data: [...]
paddle.enable_static()
"""
tensor = self.value().get_tensor()
if tensor._is_initialized():
return 'Parameter: %s\n%s' % (self.name, str(tensor))
else:
return 'Parameter: %s, not initialized' % (self.name)
return "Parameter containing:\n {}\n - stop_gradient: {}".format(
super(ParamBase, self).__str__(), self.stop_gradient)
__repr__ = __str__
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 6de463d3
......@@ -12049,8 +12049,8 @@ def logical_and(x, y, out=None, name=None):
paddle.disable_static()
x_data = np.array([True, True, False, False], dtype=np.bool)
y_data = np.array([True, False, True, False], dtype=np.bool)
x = paddle.to_variable(x_data)
y = paddle.to_variable(y_data)
x = paddle.to_tensor(x_data)
y = paddle.to_tensor(y_data)
res = paddle.logical_and(x, y)
print(res.numpy()) # [True False False False]
"""
......
python/paddle/fluid/tests/unittests/test_var_base.py
浏览文件 @ 6de463d3
......@@ -16,6 +16,7 @@ from __future__ import print_function
import unittest
from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_, in_dygraph_mode
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
......@@ -28,6 +29,74 @@ class TestVarBase(unittest.TestCase):
self.dtype = np.float32
self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
def test_to_tensor(self):
def _test_place(place):
with fluid.dygraph.guard():
x = paddle.to_tensor(
1, dtype='float32', place=place, stop_gradient=False)
self.assertTrue(np.array_equal(x.numpy(), [1.]))
self.assertEqual(x.dtype, core.VarDesc.VarType.FP32)
self.assertEqual(x.shape, [1])
self.assertEqual(x.stop_gradient, False)
self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR)
x = paddle.to_tensor(
(1, 2), dtype='float32', place=place, stop_gradient=False)
x = paddle.to_tensor(
[1, 2], dtype='float32', place=place, stop_gradient=False)
self.assertTrue(np.array_equal(x.numpy(), [1., 2.]))
self.assertEqual(x.dtype, core.VarDesc.VarType.FP32)
self.assertEqual(x.grad, None)
self.assertEqual(x.shape, [2])
self.assertEqual(x.stop_gradient, False)
self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR)
x = paddle.to_tensor(
self.array,
dtype='float32',
place=place,
stop_gradient=False)
self.assertTrue(np.array_equal(x.numpy(), self.array))
self.assertEqual(x.dtype, core.VarDesc.VarType.FP32)
self.assertEqual(x.shape, self.shape)
self.assertEqual(x.stop_gradient, False)
self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR)
y = paddle.to_tensor(x)
y = paddle.to_tensor(y, dtype='float64', place=place)
self.assertTrue(np.array_equal(y.numpy(), self.array))
self.assertEqual(y.dtype, core.VarDesc.VarType.FP64)
self.assertEqual(y.shape, self.shape)
self.assertEqual(y.stop_gradient, True)
self.assertEqual(y.type, core.VarDesc.VarType.LOD_TENSOR)
z = x + y
self.assertTrue(np.array_equal(z.numpy(), 2 * self.array))
x = paddle.to_tensor(
[1 + 2j, 1 - 2j], dtype='complex64', place=place)
y = paddle.to_tensor(x)
self.assertTrue(np.array_equal(x.numpy(), [1 + 2j, 1 - 2j]))
self.assertEqual(y.dtype, 'complex64')
self.assertEqual(y.shape, [2])
self.assertEqual(y.real.stop_gradient, True)
self.assertEqual(y.real.type, core.VarDesc.VarType.LOD_TENSOR)
with self.assertRaises(TypeError):
paddle.to_tensor('test')
with self.assertRaises(TypeError):
paddle.to_tensor(1, dtype='test')
with self.assertRaises(ValueError):
paddle.to_tensor([[1], [2, 3]])
with self.assertRaises(ValueError):
paddle.to_tensor([[1], [2, 3]], place='test')
with self.assertRaises(ValueError):
paddle.to_tensor([[1], [2, 3]], place=1)
_test_place(core.CPUPlace())
if core.is_compiled_with_cuda():
_test_place(core.CUDAPinnedPlace())
_test_place(core.CUDAPlace(0))
def test_to_variable(self):
with fluid.dygraph.guard():
var = fluid.dygraph.to_variable(self.array, name="abc")
......@@ -76,7 +145,7 @@ class TestVarBase(unittest.TestCase):
with fluid.dygraph.guard():
var = fluid.dygraph.to_variable(self.array)
self.assertEqual(var.name, 'generated_var_0')
self.assertEqual(var.name, 'generated_tensor_0')
var.name = 'test'
self.assertEqual(var.name, 'test')
......
python/paddle/framework/__init__.py
浏览文件 @ 6de463d3
......@@ -32,12 +32,14 @@ from . import random
from .random import manual_seed
from ..fluid.framework import Variable #DEFINE_ALIAS
from ..fluid.framework import ComplexVariable #DEFINE_ALIAS
from ..fluid.param_attr import ParamAttr #DEFINE_ALIAS
from ..fluid.layers.tensor import create_global_var #DEFINE_ALIAS
from ..fluid.layers.tensor import create_parameter #DEFINE_ALIAS
from ..fluid.core import CPUPlace #DEFINE_ALIAS
from ..fluid.core import CUDAPlace #DEFINE_ALIAS
from ..fluid.core import CUDAPinnedPlace #DEFINE_ALIAS
from ..fluid.core import VarBase #DEFINE_ALIAS
from paddle.fluid import core #DEFINE_ALIAS
from ..fluid.dygraph.base import no_grad #DEFINE_ALIAS
......
python/paddle/incubate/complex/tensor/math.py
浏览文件 @ 6de463d3
......@@ -262,7 +262,7 @@ def trace(x, offset=0, axis1=0, axis2=1, name=None):
case1 = np.random.randn(3, 10, 10).astype('float64') + 1j * np.random.randn(3, 10, 10).astype('float64')
paddle.disable_static()
case1 = paddle.to_variable(case1)
case1 = paddle.to_tensor(case1)
data1 = paddle.complex.trace(case1, offset=1, axis1=1, axis2=2) # data1.shape = [3]
"""
complex_variable_exists([x], "trace")
......
python/paddle/nn/functional/activation.py
浏览文件 @ 6de463d3
......@@ -461,7 +461,7 @@ def softmax(x, axis=-1, name=None):
[[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[6.0, 7.0, 8.0, 9.0]]], 'float32')
x = paddle.to_variable(x)
x = paddle.to_tensor(x)
out = F.softmax(x)
# [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
# [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
......
python/paddle/nn/layer/activation.py
浏览文件 @ 6de463d3
......@@ -283,7 +283,7 @@ class LeakyReLU(layers.Layer):
paddle.disable_static()
lrelu = paddle.nn.LeakyReLU()
x = paddle.to_variable(np.array([-2, 0, 1], 'float32'))
x = paddle.to_tensor(np.array([-2, 0, 1], 'float32'))
out = lrelu(x) # [-0.02, 0., 1.]
"""
......
python/paddle/tensor/__init__.py
浏览文件 @ 6de463d3
......@@ -22,9 +22,7 @@ from __future__ import print_function
from .random import randperm
from .attribute import rank #DEFINE_ALIAS
from .attribute import shape #DEFINE_ALIAS
from .creation import create_tensor #DEFINE_ALIAS
# from .creation import create_lod_tensor #DEFINE_ALIAS
# from .creation import create_random_int_lodtensor #DEFINE_ALIAS
from .creation import to_tensor #DEFINE_ALIAS
from .creation import crop_tensor #DEFINE_ALIAS
from .creation import diag #DEFINE_ALIAS
from .creation import eye #DEFINE_ALIAS
......@@ -179,6 +177,5 @@ from .stat import mean #DEFINE_ALIAS
from .stat import reduce_mean #DEFINE_ALIAS
from .stat import std #DEFINE_ALIAS
from .stat import var #DEFINE_ALIAS
# from .tensor import Tensor #DEFINE_ALIAS
# from .tensor import LoDTensor #DEFINE_ALIAS
# from .tensor import LoDTensorArray #DEFINE_ALIAS
python/paddle/tensor/creation.py
浏览文件 @ 6de463d3
......@@ -13,7 +13,12 @@
# limitations under the License.
from __future__ import print_function
import numpy as np
from ..fluid.framework import Variable
from ..fluid.framework import unique_name
from ..fluid.framework import _current_expected_place
from ..fluid.framework import dygraph_only
from ..fluid.initializer import Constant
from ..fluid.layers import core
from ..fluid.layer_helper import LayerHelper
......@@ -21,20 +26,16 @@ from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtyp
from ..fluid.framework import convert_np_dtype_to_dtype_, in_dygraph_mode, _varbase_creator, device_guard, OpProtoHolder
from ..fluid.layers import fill_constant
from paddle.common_ops_import import *
import paddle
# TODO: define functions to get create a tensor
from ..fluid.layers import crop_tensor #DEFINE_ALIAS
from ..fluid.layers import diag #DEFINE_ALIAS
from ..fluid.layers import fill_constant #DEFINE_ALIAS
from ..fluid.layers import create_tensor #DEFINE_ALIAS
from ..fluid.layers import linspace #DEFINE_ALIAS
import paddle
__all__ = [
'create_tensor',
# 'create_lod_tensor',
# 'create_random_int_lodtensor',
'to_tensor',
'crop_tensor',
'diag',
'fill_constant',
......@@ -54,6 +55,170 @@ __all__ = [
]
@dygraph_only
def to_tensor(data, dtype=None, place=None, stop_gradient=True):
"""
Constructs a ``paddle.Tensor`` or ``paddle.ComplexTensor`` from ``data`` ,
which can be scalar, tuple, list, numpy\.ndarray, paddle\.Tensor, paddle\.ComplexTensor.
If the ``data`` is already a tensor, and ``dtype`` or ``place`` does't change, no copy
will be performed and return origin tensor, otherwise a new tensor will be constructed
and returned. Similarly, if the data is an numpy\.ndarray of with the same ``dtype``
and the current place is cpu, no copy will be performed.
The ``ComplexTensor`` is a unique type of paddle. If x is ``ComplexTensor``, then
``x.real`` is the real part, and ``x.imag`` is the imaginary part.
Args:
data(scalar|tuple|list|ndarray|Tensor|ComplexTensor): Initial data for the tensor.
Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor, paddle\.ComplexTensor.
dtype(str, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' ,
'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8'. And
'complex64' , 'complex128' only for ComplexTensor.
Default: None, infers data type from ``data`` .
place(CPUPlace|CUDAPinnedPlace|CUDAPlace, optional): The place to allocate Tensor. Can be
CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place.
stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
Returns:
Tensor: A Tensor or ComplexTensor constructed from ``data``.
Raises:
TypeError: If the data type of ``data`` is not scalar, list, tuple, numpy.ndarray, paddle.Tensor, paddle.ComplexTensor
ValueError: If ``data`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]
TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
ValueError: If ``place`` is not paddle.Place, paddle.CUDAPinnedPlace, paddle.CUDAPlace
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.enable_imperative()
type(paddle.to_tensor(1))
# <class 'paddle.Tensor'>
paddle.to_tensor(1)
# Tensor: generated_tensor_0
# - place: CUDAPlace(0) # allocate on global default place CPU:0
# - shape: [1]
# - layout: NCHW
# - dtype: int64_t
# - data: [1]
x = paddle.to_tensor(1)
paddle.to_tensor(x, dtype='int32', place=paddle.CPUPlace()) # A new tensor will be constructed due to different dtype or place
# Tensor: generated_tensor_01
# - place: CPUPlace
# - shape: [1]
# - layout: NCHW
# - dtype: int
# - data: [1]
paddle.to_tensor((1.1, 2.2), place=paddle.CUDAPinnedPlace())
# Tensor: generated_tensor_1
# - place: CUDAPinnedPlace
# - shape: [2]
# - layout: NCHW
# - dtype: double
# - data: [1.1 2.2]
paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], place=paddle.CUDAPlace(0), stop_gradient=False)
# Tensor: generated_tensor_2
# - place: CUDAPlace(0)
# - shape: [2, 2]
# - layout: NCHW
# - dtype: double
# - data: [0.1 0.2 0.3 0.4]
type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]]), , dtype='complex64')
# <class 'paddle.ComplexTensor'>
paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
# ComplexTensor[real]: generated_tensor_0.real
# - place: CUDAPlace(0)
# - shape: [2, 2]
# - layout: NCHW
# - dtype: float
# - data: [1 2 3 4]
# ComplexTensor[imag]: generated_tensor_0.imag
# - place: CUDAPlace(0)
# - shape: [2, 2]
# - layout: NCHW
# - dtype: float
# - data: [1 0 2 0]
"""
if place is None:
place = _current_expected_place()
elif not isinstance(place,
(core.CPUPlace, core.CUDAPinnedPlace, core.CUDAPlace)):
raise ValueError(
"'place' must be any of paddle.Place, paddle.CUDAPinnedPlace, paddle.CUDAPlace"
)
#Todo(zhouwei): Support allocate tensor on any other specified card
if isinstance(place, core.CUDAPlace) and isinstance(
_current_expected_place(), core.CUDAPlace) and place._get_device_id(
) != _current_expected_place()._get_device_id():
place = _current_expected_place()
if not isinstance(data, np.ndarray):
if np.isscalar(data) and not isinstance(data, str):
data = np.array([data])
elif isinstance(data, (list, tuple)):
data = np.array(data)
if data.dtype == np.object:
raise ValueError(
"\n\tFaild to convert input data to a regular ndarray :\n\t - Usually "
"this means the input data contains nested lists with different lengths. "
)
elif isinstance(data, paddle.Tensor):
data.stop_gradient = stop_gradient
if not data.place._equals(place):
data = data._copy_to(place, False)
if dtype:
if convert_dtype(dtype) != convert_dtype(data.dtype):
return data.astype(convert_dtype(dtype))
return data
elif isinstance(data, paddle.ComplexTensor):
return data
else:
raise TypeError(
"Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|numpy.ndarray|paddle.Tensor|paddle.ComplexTensor".
format(type(data)))
if dtype:
dtype = convert_dtype(dtype)
if dtype != data.dtype:
data = data.astype(dtype)
if not np.iscomplexobj(data):
return paddle.Tensor(
value=data,
place=place,
persistable=False,
zero_copy=True,
stop_gradient=stop_gradient)
else:
name = unique_name.generate('generated_tensor')
real_tensor = paddle.Tensor(
value=data.real,
place=place,
zero_copy=True,
name=name + ".real",
stop_gradient=stop_gradient)
imag_tensor = paddle.Tensor(
value=data.imag,
place=place,
zero_copy=True,
name=name + ".imag",
stop_gradient=stop_gradient)
return paddle.ComplexTensor(real_tensor, imag_tensor)
def full_like(x, fill_value, dtype=None, name=None):
"""
:alias_main: paddle.full_like
......@@ -201,7 +366,7 @@ def ones_like(x, dtype=None, name=None):
paddle.disable_static()
x = paddle.to_variable(np.array([1,2,3], dtype='float32'))
x = paddle.to_tensor(np.array([1,2,3], dtype='float32'))
out1 = paddle.zeros_like(x) # [1., 1., 1.]
out2 = paddle.zeros_like(x, dtype='int32') # [1, 1, 1]
......@@ -291,7 +456,7 @@ def zeros_like(x, dtype=None, name=None):
paddle.disable_static()
x = paddle.to_variable(np.array([1,2,3], dtype='float32'))
x = paddle.to_tensor(np.array([1,2,3], dtype='float32'))
out1 = paddle.zeros_like(x) # [0., 0., 0.]
out2 = paddle.zeros_like(x, dtype='int32') # [0, 0, 0]
......@@ -471,7 +636,7 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
out3 = paddle.arange(4.999, dtype='float32')
# [0., 1., 2., 3., 4.]
start_var = paddle.to_variable(np.array([3]))
start_var = paddle.to_tensor(np.array([3]))
out4 = paddle.arange(start_var, 7)
# [3, 4, 5, 6]
......@@ -713,8 +878,8 @@ def meshgrid(*args, **kwargs):
input_3 = np.random.randint(0, 100, [100, ]).astype('int32')
input_4 = np.random.randint(0, 100, [200, ]).astype('int32')
tensor_3 = paddle.to_variable(input_3)
tensor_4 = paddle.to_variable(input_4)
tensor_3 = paddle.to_tensor(input_3)
tensor_4 = paddle.to_tensor(input_4)
grid_x, grid_y = paddle.tensor.meshgrid(tensor_3, tensor_4)
#the shape of grid_x is (100, 200)
......
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