* Don't allow requires_grad to be set on integer Tensor constructors in tensor_new.
* Fix autograd test.
* Fix test_distributions.
* Fix test_jit.
* Fix NN tests.
* 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.
* Make AT_ASSERT/AT_ERROR non-printf based, other tweaks
- AT_ASSERT/AT_ERROR don't take printf strings anymore; instead,
they take a comma-separated list of things you wanted to print
(bringing it inline with Caffe2's conventions).
Instead of AT_ASSERT(x == 0, "%d is not zero", x)
you write AT_ASSERT(x == 0, x, " is not zero")
This is done by way of a new variadic template at::str(), which
takes a list of arguments and cats their string reps (as per
operator<<) together.
- A bunch of the demangling logic that was in Error.h is now
moved to Error.cpp (better header hygiene.) Also, demangle
has been moved out to its own helper function, and also
a new helper demangle_type (from Caffe2) added.
- A bunch of AT_ASSERT converted into AT_CHECK, to more properly
convey which checks can be caused by user error, and which are
due to logic error in ATen.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* CR
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Fix test failure.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* buildfix
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* More fixes.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* One more fix
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Try harder
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Fix torch.tensor(...) device-type calculation when used with numpy and type inference.
* Fix tensor device type inference as well.
* Better variable type inference: infer cuda-ness only if device is not specified.
* Use Index rather than Long for IntList, so floating-point types convertible to ints fail the parsing.
Basically, our unpackLong code works with floating-point types that are convertible to ints, but this isn't often what you want (because of truncation).
What you actually want is to convert to an index, which will usually find such issues.
I made this the minimal change I could because:
1) I didn't want to change unpackLong because the existing code call checkLong before unpackLong, so this should be a non-issue most of the time. And fixing this properly requires calling checkLong again, which will slow everything down.
2) An exception above is with IntList, which only checks that 1) it is a tuple or 2) it is a varargs tuple (i.e. torch.ones(1, 2, 3)).
* Fix bug.
* Don't conflict tensor and IntList bindings.
* Change function to be consistent between python 2 and 3.
* Check Index.
* Move IntList overloads in legacy new functions to below Tensor overloads.
* Separate cuda-ness from dtype.
There are no longer torch.cuda.int64, etc; only torch.int64 that correspond to at::ScalarType.
At the python arg parser level, the corresponding ATen type is selected from the combination of (ScalarType, Layout, Device).
There is also currently unused code in here for support ScalarType in native_functions; this will be used for specifying aggregate types
on reduction functions.
* Fix test_autograd.
* Add defaults to randint_like.
* Track is_cuda in py tensor types.
* Fix test_sparse.
* Fix multiprocessing.
* Fix rnn.
* Fix test_nn.
* Fix flake8.
* Add string-style devices to all tensors.
Previously, tensors only had a 'get_device' method which would throw an exception on a CPU tensor. This made it necessary to if/else code that
was meant to be device agnostic.
This PR implements the following:
1) Adds a 'device' property to all tensors that returns a string representation of the device for all tensors.
For cpu tensors this is 'cpu'. For cuda tensors this is 'cuda:X', where X is the cuda device ordinal.
2) Adds a DeviceSpec class. This is just a helper class for separating device_type and device_index specification and to allow partial specification.
For example, you can call DeviceSpec('cuda'), DeviceSpec('cuda:0'), DeviceSpec('cuda', 1).
Also has backwards compatibility support for specifying integers, which are treated as cuda devices.
DeviceSpecs have the following properties:
a) device_type: string representation of the device type (i.e. 'cpu' or 'cuda')
b) device_index: integer for the device index (None if not specified)
c) cuda_device_index: for backwards compatibility; behaves roughly like `get_device` did previously. I.e. if a function previously took integers for cuda devices,
it can now take DeviceSpecs (or strings), and can maintain the old functionality by calling `old_index = DeviceSpec(old).cuda_device_index`.
3) tensor methods and torch. functions that took integer devices can now take integers, strings, or DeviceSpecs. For example:
torch.randn((2,3), dtype=torch.cuda.float32, device='cuda:1')
TODO in future PRs:
A) Split out cuda from dtype so you don't need to overspecify cuda-ness
B) We currently only support strings/DeviceSpecs in tensor methods and torch. functions. We should have equivalents torch.cuda.device(...), torch.cuda.device_of, etc.
at the torch. level that work on strings/DeviceSpecs
* Add deviceInt64 to python arg parser.
* device_str.
* Remove device_str.
* remove device prefix from attributes.
* Use const char * instead of string.
* Move autogpu index out of Device.
* comment on is_default.
* Rename torch.DeviceSpec to torch.device.
* comment.
* Fix tests.
* Fix flake8.
* Fix sparse_coo_tensor parameter name.
* Improve error message.
* Remove device_ prefix from C++ device object.
* Allocate static strings.
* Return not implemented from rich compare.
* Move torch::Device to THPDevice.
* Remove cuda index.
* Py_RETURN_NOTIMPLEMENTED doesn't exist in python2.
* Introduce torch.layout and split layout from dtypes.
Tensors (and tensor types) now have a 'layout' attribute that returns either 'torch.strided' or 'torch.sparse_coo'.
Previously, dtypes were 1-to-1 with ATen types/PyTensorTypes; the impetus behind this decision was to make things easy in the common case
(i.e. specifying a type in a factory function). But this doesn't really follow for sparity, which isn't a common case.
It also doesn't properly represent the concept or a dtype, which in numpy are proper scalar types (i.e. roughly the type returned from indexing the
last dimension of an n-d array). But this should be the same whether or not the tensor is represented via strides, sparsity, etc.
This is accomplished by:
1) having the dtype of tensor return the (device-type, scalar-type) combination, i.e. torch.cuda.float32, so both
torch.cuda.FloatTensor and torch.cuda.sparse.FloatTensor have the same dtype
2) Adding a layout parameter to python functions, where the combination of (dtype, layout) maps to an ATen type that is used for dispatch.
* Formatting, make init throw python_error.
* Fix cuda not enabled error message.
* Fix test.
This is in preparation for splitting out sparsity (layout) from dtypes; it's complex to maintain these
and tensor.new(...) is a legacy API in any case.
* Add numpy.array-like type inference to torch.tensor.
* Temporary fix for int/double types.
* Treat python floats as the default (scalar) dtype.
* Also make 0-length sequences the default scalar type and add more tests.
* Add type inference to sparse_coo_tensor.
* Fix sparse test.
* Remove allow_variables.
* Check numpy platform bits.
* Address review comments.
* Make suggested changes to constraints.
* More checking windows builds.
* Fix test for windows.
* Add torch.sparse_coo_tensor factory.
Notes:
1) I didn't add Tensor.new_sparse_coo_tensor; it didn't seem particularly useful, but it's easy to add
2) This doesn't do the type inference, i.e. torch.sparse_coo_tensor(indices=LongTensor, values=IntTensor)
will return a sparse tensor corresponding to the default type rather than a sparse IntTensor. We can add
type inference later when we add it to other factories.
* Fix merge.
* Use type_conversion function from python_variable_methods.
Notes:
1) I didn't add Tensor.new_sparse_coo_tensor; it didn't seem particularly useful, but it's easy to add
2) This doesn't do the type inference, i.e. torch.sparse_coo_tensor(indices=LongTensor, values=IntTensor)
will return a sparse tensor corresponding to the default type rather than a sparse IntTensor. We can add
type inference later when we add it to other factories.
* Add torch.empty, torch.full and new_ size Tensor factory methods.
This adds torch.full, torch.empty equivalents of np.full, np.empty.
In addition, this adds size-based Tensor factory methods new_empty, new_ones, new_full, new_zeros,
which is meant to complete the separation of the legacy "new" method into data-based and size-based
functions.
This also fixes an issue in sparse zeros_like when the dtype didn't match the argument dtype.
* Get rid of unnecessary zero in sparse tensor zeros_like.
* Fix test if only 1 cuda device.
- Remove some uses of mega-header THP.h
- Use HANDLE_TH_ERRORS in functions that may throw
- Move NumPy includes to common header
- Delete unused allocator
* Revert "Fix wrong argument name (#5366)"
This reverts commit cc9d3b265d.
* Fix wrong argument naming
* Revert "Wrap torch::cuda::lazy_init with WITH_CUDA flag"
This reverts commit a8fa37f8fac5aef09eb7fe54d84de6126618c262.
* Revert "Solves the linking error related to lazy_init for MSVC"
This reverts commit 63913a102f274865a76e7c40ffdf6b40c277d5ff.
* better solution for the linking error related to lazy_init for MSVC
* Naming changes
* Namespace changes and further comment
* Rebasing onto current master
* Remove code that is useless
* Fix linting
* Remove rebasing bugs
* Support dtypes in legacy new constructors.
* Add comment about why we don't have dtype for sparse (indices, values).
* separate legacy tensor ctor vs new (new includes dtypes).
* Use TypeError.
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
* Revert "Fix wrong argument name (#5366)"
This reverts commit cc9d3b265d.
* Solves the linking error related to lazy_init for MSVC
* Fix wrong argument naming
* Wrap torch::cuda::lazy_init with WITH_CUDA flag
* Various dtype improvements.
1) Add dtypes to the new data-based constructors: Variable.new_tensor and torch.autograd.variable.
2) In the python signatures, use Type instead of Dtype to match the C++ signatures; the error messages still print as dtype.
3) Handle / add a better error message when a dtype is used when ATen was not compiled with that type (e.g. cuda types).
4) Move cuda_lazy_init to its own file.
A later commit will add support to the legacy constructors as well.
* Move implementation of lazy_init to cpp.
* Fix parsed_arg size.
* Improve Variable interface
* Address comments from @apaszke and @colesbury
* string ::operator= is not noexcept
* Remove ir.h from tracer_state.h to improve build times
* Make Variable a struct and pack SavedVariable fields
* Implement as_variable_ref
* grad_fn_ptr() -> grad_fn_unsafe()
* Reduce hackiness of set_type hack
* Include variable.h and edge.h in tracer_state.h because it uses them
* class Variable -> struct Variable because Windows cant even
* Make Variable::output_nr uint32_t instead of int
* Add comment about tracing state
* Replaced more static_cast<Variable&> and improve docs
* Remove SavedVariable destructor and construct members in init list
* Clarify docs for Variable
* Variable::set_version -> set_version_counter
This better maintains backwards compatibility when Tensors and Variables
are merged. For example:
>>> loss = var.sum().data[0]
Currently, `var.sum().data` is 1-dim so indexing. Once scalars are
enabled and Variable and Tensor are merged it will be zero-dim. This
change allows that expression to continue working (with a warning). In
the future, the canonical way to compute that expression will be:
>>> loss = float(var.sum())
Or an equivalent alternative:
>>> loss = var.sum().item()
Also fixes a few error cases.
* Add a new_tensor instance method to Variable that takes only data.
This is to work around the legacy problems of new, where e.g.
new(5) will give you an unfilled tensor rather than a scalar.
* Remove double return.
* Fix cuda scalar code path.
* Work around lack of WITH_SCALARS.
* Implement a (data-only) Variable factory.
Implements a function, torch.autograd.variable that is modeled after np.array. The main difference between it and new() and
the tensor constructors is it inteprets a python number as data, i.e. as a 0-dimensional tensor (we currently don't expose
that at the pytorchl level, so it will temporarily end up as a 1-dimensional tensor), rather than a size.
The main difference currently between torch.autograd.variable and np.array is that np.autograd.variable is stricter, e.g.
passing a PyFloat when an integral type is the default tensor type will result in an array; np.array basically lets anything
through (floating-point / integral mismatch, overflow, etc). This is to keep it consistent with Variable.new when called with
a sequence, although we can loosen the checks later.
This will be renamed to torch.tensor once we merge Variable and tensor.
* Address review comments.
