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gchanangchanan
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Implement torch.as_tensor, similar to numpy.asarray. (#7109)
* Implement torch.as_tensor, similar to numpy.asarray. torch.as_tensor behaves like torch.tensor except it avoids copies if possible; so also somewhat like tensor.new but without the size overloads. I didn't add a requires_grad field, because we haven't decided on the semantics such as as_param. * Remove requires_grad for doc.
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‎docs/source/tensors.rst‎

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@@ -44,7 +44,7 @@ A tensor can be constructed from a Python :class:`list` or sequence using the
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:meth:`~torch.Tensor.requires_grad_` or
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:meth:`~torch.Tensor.detach` to avoid a copy.
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If you have a numpy array and want to avoid a copy, use
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:func:`torch.from_numpy`.
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:func:`torch.as_tensor`.
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An tensor of specific data type can be constructed by passing a
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:class:`torch.dtype` and/or a :class:`torch.device` to a

‎docs/source/torch.rst‎

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@@ -33,6 +33,7 @@ Creation Ops
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range of distributions.
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.. autofunction:: tensor
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.. autofunction:: as_tensor
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.. autofunction:: from_numpy
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.. autofunction:: zeros
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.. autofunction:: zeros_like

‎test/test_torch.py‎

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@@ -2043,6 +2043,45 @@ def test_new_tensor(self):
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self.assertIs(torch.int, res1.dtype)
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self.assertEqual(res1.get_device(), expected.get_device())
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def test_as_tensor(self):
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# from python data
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x = [[0, 1], [2, 3]]
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self.assertEqual(torch.tensor(x), torch.as_tensor(x))
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self.assertEqual(torch.tensor(x, dtype=torch.float32), torch.as_tensor(x, dtype=torch.float32))
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# from tensor (doesn't copy unless type is different)
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y = torch.tensor(x)
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self.assertIs(y, torch.as_tensor(y))
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self.assertIsNot(y, torch.as_tensor(y, dtype=torch.float32))
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if torch.cuda.is_available():
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self.assertIsNot(y, torch.as_tensor(y, device='cuda'))
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y_cuda = y.to('cuda')
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self.assertIs(y_cuda, torch.as_tensor(y_cuda))
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self.assertIs(y_cuda, torch.as_tensor(y_cuda, device='cuda'))
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if TEST_NUMPY:
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# doesn't copy
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n = np.random.rand(5, 6)
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n_astensor = torch.as_tensor(n)
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self.assertEqual(torch.tensor(n), n_astensor)
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n_astensor[0][0] = 250.7
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self.assertEqual(torch.tensor(n), n_astensor)
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# changing dtype causes copy
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n = np.random.rand(5, 6).astype(np.float32)
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n_astensor = torch.as_tensor(n, dtype=torch.float64)
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self.assertEqual(torch.tensor(n, dtype=torch.float64), n_astensor)
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n_astensor[0][1] = 250.8
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self.assertNotEqual(torch.tensor(n, dtype=torch.float64), n_astensor)
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# changing device causes copy
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if torch.cuda.is_available():
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n = np.random.randn(5, 6)
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n_astensor = torch.as_tensor(n, device='cuda')
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self.assertEqual(torch.tensor(n, device='cuda'), n_astensor)
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n_astensor[0][2] = 250.9
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self.assertNotEqual(torch.tensor(n, device='cuda'), n_astensor)
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def test_diag(self):
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x = torch.rand(100, 100)
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res1 = torch.diag(x)

‎tools/autograd/templates/python_torch_functions.cpp‎

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@@ -136,6 +136,13 @@ static PyObject * THPVariable_arange(PyObject* self, PyObject* args, PyObject* k
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPVariable_as_tensor(PyObject* self, PyObject* args, PyObject* kwargs)
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{
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HANDLE_TH_ERRORS
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return THPVariable_Wrap(torch::utils::as_tensor(default_type(), args, kwargs));
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END_HANDLE_TH_ERRORS
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}
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// The Python clamp() syntax has to be mapped to one of three C++ functions
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static PyObject * THPVariable_clamp(PyObject* module, PyObject* args, PyObject* kwargs)
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{
@@ -215,6 +222,7 @@ static PyObject * THPVariable_tensor(PyObject* self, PyObject* args, PyObject* k
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static PyMethodDef torch_functions[] = {
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{"arange", (PyCFunction)THPVariable_arange, METH_VARARGS | METH_KEYWORDS | METH_STATIC, NULL},
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{"as_tensor", (PyCFunction)THPVariable_as_tensor, METH_VARARGS | METH_KEYWORDS | METH_STATIC, NULL},
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{"clamp", (PyCFunction)THPVariable_clamp, METH_VARARGS | METH_KEYWORDS | METH_STATIC, NULL},
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{"dsmm", (PyCFunction)THPVariable_mm, METH_VARARGS | METH_KEYWORDS | METH_STATIC, NULL},
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{"from_numpy", (PyCFunction)THPVariable_from_numpy, METH_STATIC | METH_O, NULL},

‎torch/_torch_docs.py‎

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@@ -46,6 +46,19 @@ def parse_kwargs(desc):
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returned tensor. Default: ``False``.
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""")
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factory_data_common_args = parse_kwargs("""
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data (array_like): Initial data for the tensor. Can be a list, tuple,
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NumPy ``ndarray``, scalar, and other types.
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dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
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Default: if None, infers data type from :attr:`data`.
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device (:class:`torch.device`, optional): the desired device of returned tensor.
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Default: if None, uses the current device for the default tensor type
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(see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
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for CPU tensor types and the current CUDA device for CUDA tensor types.
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requires_grad (bool, optional): If autograd should record operations on the
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returned tensor. Default: ``False``.
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""")
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add_docstr(torch.abs,
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r"""
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abs(input, out=None) -> Tensor
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[ 3., 6.]])
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""")
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add_docstr(torch.as_tensor,
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r"""
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as_tensor(data, dtype=None, device=None) -> Tensor
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Convert the data into a `torch.Tensor`. If the data is already a `Tensor` of the same `dtype` and `device`, no copy
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will be performed. Similarly, if the data is an ``ndarray`` of the corresponding `dtype` and the `device` is the cpu,
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no copy will be performed.
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Args:
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{data}
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{dtype}
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{device}
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Example::
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>>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
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tensor([[ 0.1000, 1.2000],
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[ 2.2000, 3.1000],
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[ 4.9000, 5.2000]])
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>>> a = numpy.array([1, 2, 3])
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>>> t = torch.from_numpy(a)
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>>> t
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tensor([ 1, 2, 3])
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>>> t[0] = -1
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>>> a
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array([-1, 2, 3])
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""".format(**factory_data_common_args))
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add_docstr(torch.asin,
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r"""
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asin(input, out=None) -> Tensor
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:func:`torch.from_numpy`.
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Args:
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data (array_like): Initial data for the tensor. Can be a list, tuple,
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NumPy ``ndarray``, scalar, and other types.
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dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
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Default: if None, infers data type from :attr:`data`.
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device (:class:`torch.device`, optional): the desired device of returned tensor.
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Default: if None, uses the current device for the default tensor type
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(see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
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for CPU tensor types and the current CUDA device for CUDA tensor types.
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requires_grad (bool, optional): If autograd should record operations on the
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returned tensor. Default: ``False``.
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{data}
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{dtype}
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{device}
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{requires_grad}
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Example::
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>>> torch.tensor([]) # Create an empty tensor (of size (0,))
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tensor([])
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""")
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""".format(**factory_data_common_args))
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add_docstr(torch.range,
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r"""

‎torch/csrc/utils/tensor_new.cpp‎

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@@ -442,6 +442,21 @@ Tensor tensor_ctor(const Type& type, PyObject* args, PyObject* kwargs) {
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throw std::runtime_error("tensor(): invalid arguments");
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}
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Tensor as_tensor(const Type& type, PyObject* args, PyObject* kwargs) {
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// TODO: add requires_grad once we decide on semantics for sharing data.
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static PythonArgParser parser({
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"as_tensor(PyObject* data, *, ScalarType dtype=None, Device? device=None)",
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});
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ParsedArgs<3> parsed_args;
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auto r = parser.parse(args, kwargs, parsed_args);
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if (r.idx == 0) {
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bool type_inference = r.isNone(1);
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return internal_new_from_data(
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typeWithDefault(r, 1, 2, type), r.deviceOptional(2), r.pyobject(0), false, false, type_inference);
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}
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throw std::runtime_error("tensor(): invalid arguments");
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}
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Tensor new_tensor(const Type& type, PyObject* args, PyObject* kwargs) {
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static PythonArgParser parser({

‎torch/csrc/utils/tensor_new.h‎

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@@ -11,6 +11,7 @@ at::Tensor legacy_tensor_new(const at::Type& type, PyObject* args, PyObject* kwa
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at::Tensor legacy_new_from_data(const at::Type& type, at::optional<Device> device, PyObject *data);
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at::Tensor sparse_coo_tensor_ctor(const at::Type& type, PyObject* args, PyObject* kwargs);
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at::Tensor tensor_ctor(const at::Type& type, PyObject* args, PyObject* kwargs);
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at::Tensor as_tensor(const at::Type& type, PyObject* args, PyObject* kwargs);
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at::Tensor new_tensor(const at::Type& type, PyObject* args, PyObject* kwargs);
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at::Tensor new_empty(const at::Type& type, PyObject* args, PyObject* kwargs);
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at::Tensor new_full(const at::Type& type, PyObject* args, PyObject* kwargs);

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