| import sys |
| import torch._C as _C |
| from collections import OrderedDict |
| import torch.utils.hooks as hooks |
| |
| from ._functions import * |
| |
| |
| class Variable(_C._VariableBase): |
| """Wraps a tensor and records the operations applied to it. |
| |
| Variable is a thin wrapper around a Tensor object, that also holds |
| the gradient w.r.t. to it, and a reference to a function that created it. |
| This reference allows retracing the whole chain of operations that |
| created the data. If the Variable has been created by the user, its creator |
| will be ``None`` and we call such objects *leaf* Variables. |
| |
| Since autograd only supports scalar valued function differentiation, grad |
| size always matches the data size. Also, grad is normally only allocated |
| for leaf variables, and will be always zero otherwise. |
| |
| Attributes: |
| data: Wrapped tensor of any type. |
| grad: Variable holding the gradient of type and location matching |
| the ``.data``. This attribute is lazily allocated and can't |
| be reassigned. |
| requires_grad: Boolean indicating whether the Variable has been |
| created by a subgraph containing any Variable, that requires it. |
| See :ref:`excluding-subgraphs` for more details. |
| Can be changed only on leaf Variables. |
| volatile: Boolean indicating that the Variable should be used in |
| inference mode, i.e. don't save the history. See |
| :ref:`excluding-subgraphs` for more details. |
| Can be changed only on leaf Variables. |
| creator: Function of which the variable was an output. For leaf |
| (user created) variables it's ``None``. Read-only attribute. |
| |
| Parameters: |
| data (any tensor class): Tensor to wrap. |
| requires_grad (bool): Value of the requires_grad flag. **Keyword only.** |
| volatile (bool): Value of the volatile flag. **Keyword only.** |
| """ |
| |
| _fallthrough_methods = { |
| 'size', |
| 'stride', |
| 'nelement', |
| 'ndimension', |
| 'element_size', |
| 'is_contiguous', |
| 'is_set_to', |
| 'is_signed', |
| 'numel', |
| 'dim', |
| 'get_device', |
| 'is_cuda', |
| } |
| |
| def __getattr__(self, name): |
| if name in self._fallthrough_methods: |
| return getattr(self.data, name) |
| raise AttributeError(name) |
| |
| def __getitem__(self, key): |
| if (isinstance(key, Variable) and |
| type(key.data).__name__ == 'ByteTensor'): |
| return MaskedSelect()(self, key) |
| return Index(key)(self) |
| |
| def __setitem__(self, key, value): |
| if (isinstance(key, Variable) and |
| type(key.data).__name__ == 'ByteTensor'): |
| if isinstance(value, Variable): |
| return MaskedCopy(inplace=True)(self, key, value) |
| else: |
| return MaskedFill(value, inplace=True)(self, key) |
| else: |
| if isinstance(value, Variable): |
| return SetItem(key)(self, value) |
| else: |
| return SetItem(key, value)(self) |
| |
| def __deepcopy__(self, memo): |
| if self.creator is not None: |
| raise RuntimeError("Only Variables created explicitly by the user " |
| "(graph leaves) support the deepcopy protocol at the moment") |
| result = type(self)(self.data.clone()) |
| result.requires_grad = self.requires_grad |
| result.volatile = self.volatile |
| memo[id(self)] = result |
| return result |
| |
| def __reduce_ex__(self, proto): |
| state = (self.requires_grad, self.volatile, self._backward_hooks) |
| if proto > 1: |
| return type(self), (self.data,), state |
| if sys.version_info[0] == 2: |
| from copy_reg import __newobj__ |
| else: |
| from copyreg import __newobj__ |
| return __newobj__, (type(self), self.data), state |
| |
| def __setstate__(self, state): |
| if len(state) == 5: |
| # legacy serialization of Variable |
| self.data = state[0] |
| state = (state[3], state[4], state[2]) |
| if self.creator is not None: |
| raise RuntimeError('__setstate__ can be only called on leaf variables') |
| self.requires_grad, self.volatile, self._backward_hooks = state |
| |
| def __repr__(self): |
| return 'Variable containing:' + self.data.__repr__() |
| |
| def backward(self, gradient=None, retain_variables=False): |
| """Computes the gradient of current variable w.r.t. graph leaves. |
| |
| The graph is differentiated using the chain rule. If the variable is |
| non-scalar (i.e. its data has more than one element) and requires |
| gradient, the function additionaly requires specifying ``gradient``. |
| It should be a tensor of matching type and location, that containins |
| the gradient of the differentiated function w.r.t. ``self``. |
| |
| This function accumulates gradients in the leaves - you might need to zero |
| them before calling it. |
| |
| Arguments: |
| gradient (Tensor): Gradient of the differentiated function |
| w.r.t. the data. Required only if the data has more than one |
| element. Type and location should match these of ``self.data``. |
| retain_variables (bool): If ``True``, buffers necessary for computing |
| gradients won't be freed after use. It is only necessary to |
| specify ``True`` if you want to differentiate some subgraph multiple |
| times (in some cases it will be much more efficient to use |
| `autograd.backward`). |
| """ |
| if self.volatile: |
| raise RuntimeError('calling backward on a volatile variable') |
| if gradient is None and self.requires_grad: |
| if self.data.numel() != 1: |
| raise RuntimeError( |
| 'backward should be called only on a scalar (i.e. 1-element tensor) ' |
| 'or with gradient w.r.t. the variable') |
| gradient = self.data.new().resize_as_(self.data).fill_(1) |
| self._execution_engine.run_backward((self,), (gradient,), retain_variables) |
| |
| def register_hook(self, hook): |
| """Registers a backward hook. |
| |
| The hook will be called every time a gradient with respect to the |
| variable is computed. The hook should have the following signature:: |
| |
| hook(grad) -> Tensor or None |
| |
| The hook should not modify its argument, but it can optionally return |
| a new gradient which will be used in place of :attr:`grad`. |
| |
| This function returns a handle with a method ``handle.remove()`` |
| that removes the hook from the module. |
| |
| Example: |
| >>> v = Variable(torch.Tensor([0, 0, 0]), requires_grad=True) |
| >>> h = v.register_hook(lambda grad: grad * 2) # double the gradient |
| >>> v.backward(torch.Tensor([1, 1, 1])) |
| >>> v.grad.data |
| 2 |
| 2 |
| 2 |
| [torch.FloatTensor of size 3] |
| >>> h.remove() # removes the hook |
| """ |
| if self.volatile: |
| raise RuntimeError("cannot register a hook on a volatile variable") |
| if not self.requires_grad: |
| raise RuntimeError("cannot register a hook on a variable that " |
| "doesn't require gradient") |
| if self._backward_hooks is None: |
| self._backward_hooks = OrderedDict() |
| if self.creator is not None: |
| self.creator._register_hook_dict(self) |
| handle = hooks.RemovableHandle(self._backward_hooks) |
| self._backward_hooks[id(handle)] = hook |
| return handle |
| |
| def _do_backward(self, grad_output, retain_variables): |
| assert len(grad_output) == 1 |
| assert self._version == 0 and self.creator is None, \ |
| "leaf variable was used in an inplace operation" |
| unpacked_grad = grad_output[0] |
| if self._backward_hooks: |
| for hook in self._backward_hooks.values(): |
| result = hook(unpacked_grad) |
| if result is not None: |
| unpacked_grad = result |
| self.grad.data.add_(unpacked_grad) |
| return tuple() |
| |
| def reinforce(self, reward): |
| """Registers a reward obtained as a result of a stochastic process. |
| |
| Differentiating stochastic nodes requires providing them with reward |
| value. If your graph contains any stochastic operations, you should |
| call this function on their outputs. Otherwise an error will be raised. |
| |
| Parameters: |
| reward(Tensor): Tensor with per-element rewards. It has to match |
| the device location and shape of Variable's data. |
| """ |
| if not isinstance(self.creator, StochasticFunction): |
| raise RuntimeError("reinforce() can be only called on outputs " |
| "of stochastic functions") |
| self.creator._reinforce(reward) |
| |
| def detach(self): |
| """Returns a new Variable, detached from the current graph. |
| |
| Result will never require gradient. If the input is volatile, the output |
| will be volatile too. |
| |
| .. note:: |
| |
| Returned Variable uses the same data tensor, as the original one, and |
| in-place modifications on either of them will be seen, and may trigger |
| errors in correctness checks. |
| """ |
| result = NoGrad()(self) # this is needed, because it merges version counters |
| result._creator = None |
| return result |
| |
| def detach_(self): |
| """Detaches the Variable from the graph that created it, making it a leaf.""" |
| self._creator = None |
| self.requires_grad = False |
| |
| def contiguous(self): |
| self.data = self.data.contiguous() |
| return self |
| |
| def clone(self): |
| return Clone()(self) |
| |
| def type(self, t): |
| if t != type(self.data): |
| return Type(t)(self) |
| return self |
| |
| def _get_type(self, name): |
| module = torch._import_dotted_name(self.data.__module__) |
| return getattr(module, name) |
| |
| def cuda(self, device_id=None, async=False): |
| return CudaTransfer(device_id, async)(self) |
| |
| def cpu(self): |
| return self.type(getattr(torch, type(self.data).__name__)) |
| |
| def double(self): |
| return self.type(self._get_type('DoubleTensor')) |
| |
| def float(self): |
| return self.type(self._get_type('FloatTensor')) |
| |
| def half(self): |
| return self.type(self._get_type('HalfTensor')) |
| |
| def long(self): |
| return self.type(self._get_type('LongTensor')) |
| |
| def int(self): |
| return self.type(self._get_type('IntTensor')) |
| |
| def short(self): |
| return self.type(self._get_type('ShortTensor')) |
| |
| def char(self): |
| return self.type(self._get_type('CharTensor')) |
| |
| def byte(self): |
| return self.type(self._get_type('ByteTensor')) |
| |
| def is_same_size(self, other_var): |
| return self.data.is_same_size(other_var.data) |
| |
| def _add(self, other, inplace): |
| if isinstance(other, Variable): |
| return Add(inplace)(self, other) |
| else: |
| assert not torch.is_tensor(other) |
| return AddConstant(other, inplace)(self) |
| |
| def add(self, other): |
| return self._add(other, False) |
| |
| def add_(self, other): |
| return self._add(other, True) |
| |
| def _sub(self, other, inplace): |
| if isinstance(other, Variable): |
| return Sub(inplace=inplace)(self, other) |
| else: |
| assert not torch.is_tensor(other) |
| return SubConstant(other, inplace=inplace)(self) |
| |
| def sub(self, other): |
| return self._sub(other, False) |
| |
| def sub_(self, other): |
| return self._sub(other, True) |
| |
| def mul(self, other): |
| if isinstance(other, Variable): |
| return Mul()(self, other) |
| else: |
| assert not torch.is_tensor(other) |
| return MulConstant(other)(self) |
| |
| def mul_(self, other): |
| if not isinstance(other, Variable) and not torch.is_tensor(other): |
| return MulConstant(other, inplace=True)(self) |
| raise RuntimeError("mul_ only supports scalar multiplication") |
| |
| def div(self, other): |
| if isinstance(other, Variable): |
| return Div()(self, other) |
| else: |
| assert not torch.is_tensor(other) |
| return DivConstant(other)(self) |
| |
| def div_(self, other): |
| if not isinstance(other, Variable) and not torch.is_tensor(other): |
| return DivConstant(other, inplace=True)(self) |
| raise RuntimeError("div_ only supports scalar multiplication") |
| |
| def pow(self, other): |
| if isinstance(other, Variable): |
| return Pow()(self, other) |
| else: |
| assert not torch.is_tensor(other) |
| return PowConstant(other)(self) |
| |
| def exp(self): |
| return Exp()(self) |
| |
| def exp_(self): |
| return Exp(inplace=True)(self) |
| |
| def log(self): |
| return Log()(self) |
| |
| def log1p(self): |
| return Log1p()(self) |
| |
| def neg(self): |
| return Negate()(self) |
| |
| def neg_(self): |
| return Negate(inplace=True)(self) |
| |
| def tanh(self): |
| return Tanh()(self) |
| |
| def tanh_(self): |
| return Tanh(True)(self) |
| |
| def sigmoid(self): |
| return Sigmoid()(self) |
| |
| def sigmoid_(self): |
| return Sigmoid(True)(self) |
| |
| def sin(self): |
| return Sin()(self) |
| |
| def cos(self): |
| return Cos()(self) |
| |
| def tan(self): |
| return Tan()(self) |
| |
| def asin(self): |
| return Asin()(self) |
| |
| def acos(self): |
| return Acos()(self) |
| |
| def atan(self): |
| return Atan()(self) |
| |
| def sinh(self): |
| return Sinh()(self) |
| |
| def cosh(self): |
| return Cosh()(self) |
| |
| def abs(self): |
| return Abs()(self) |
| |
| def clamp(self, min=None, max=None): |
| if min is None and max is None: |
| raise ValueError("clamp requires specifying at least one of " |
| "min and max arguments") |
| elif min is None and max is not None: |
| return CminConstant(max)(self) |
| elif min is not None and max is None: |
| return CmaxConstant(min)(self) |
| else: |
| return Clamp(min, max)(self) |
| |
| def reciprocal(self): |
| return Reciprocal()(self) |
| |
| def floor(self): |
| return Floor()(self) |
| |
| def ceil(self): |
| return Ceil()(self) |
| |
| def frac(self): |
| return Frac()(self) |
| |
| def sqrt(self): |
| return Sqrt()(self) |
| |
| def round(self): |
| return Round()(self) |
| |
| def sign(self): |
| return Sign()(self) |
| |
| def trunc(self): |
| return Trunc()(self) |
| |
| def fmod(self, value): |
| return Fmod(value)(self) |
| |
| def remainder(self, value): |
| return Remainder(value)(self) |
| |
| def lerp(self, tensor, weight): |
| return Lerp(weight)(self, tensor) |
| |
| def rsqrt(self): |
| return Rsqrt()(self) |
| |
| def sum(self, dim=None): |
| return Sum(dim)(self) |
| |
| def prod(self, dim=None): |
| return Prod(dim)(self) |
| |
| def mean(self, dim=None): |
| return Mean(dim)(self) |
| |
| def max(self, dim=None): |
| if isinstance(dim, Variable): |
| return Cmax()(self, dim) |
| return Max(dim)(self) |
| |
| def min(self, dim=None): |
| if isinstance(dim, Variable): |
| return Cmin()(self, dim) |
| return Min(dim)(self) |
| |
| def mode(self, dim): |
| return Mode(dim)(self) |
| |
| def median(self, dim): |
| return Median(dim)(self) |
| |
| def kthvalue(self, dim): |
| return Kthvalue(dim)(self) |
| |
| def sort(self, dim=None, descending=False): |
| return Sort(dim, descending)(self) |
| |
| def topk(self, k, dim=None, largest=True, sorted=True): |
| return Topk(k, dim, largest, sorted)(self) |
| |
| def view(self, *sizes): |
| return View(*sizes)(self) |
| |
| def view_as(self, tensor): |
| return View(*tensor.size())(self) |
| |
| def split(self, split_size, dim=0): |
| return torch.split(self, split_size, dim) |
| |
| def repeat(self, *repeats): |
| if len(repeats) == 1 and isinstance(repeats[0], torch.Size): |
| repeats = repeats[0] |
| else: |
| repeats = torch.Size(repeats) |
| return Repeat(repeats)(self) |
| |
| def var(self, dim=None, unbiased=True): |
| mean = self.mean(dim) |
| if dim is None: |
| mean = mean.view(*(1 for s in self.size())) |
| mean_expanded = mean.expand_as(self) |
| zero_centered = self.sub(mean_expanded) |
| var = zero_centered.mul(zero_centered).sum(dim) |
| numel = self.numel() if dim is None else self.size(dim) |
| return var.div(numel - int(unbiased)) |
| |
| def std(self, dim=None, unbiased=True): |
| return self.var(dim, unbiased).sqrt() |
| |
| def renorm(self, norm_type, dim, maxnorm): |
| t = self.transpose(dim, 0) |
| flat = t.contiguous().view(self.size(0), -1) |
| norms = flat.norm(norm_type, 1) |
| norms = norms.clamp(max=maxnorm).div(norms.add(1e-7)) |
| flat_out = flat.mul(norms.expand_as(flat)) |
| return flat_out.view(t.size()).transpose(dim, 0) |
| |
| @staticmethod |
| def _static_blas(cls, args, inplace): |
| num_args = len(args) |
| alpha = beta = 1 |
| if num_args > 5: |
| raise RuntimeError("too many args") |
| if num_args == 5: |
| alpha, beta = args[1:3] |
| if num_args == 4: |
| alpha = args[1] |
| return cls(alpha, beta, inplace)(*(args[:1] + args[-2:])) |
| |
| def _blas(self, cls, args, inplace): |
| return self._static_blas(cls, (self,) + args, inplace) |
| |
| def mm(self, matrix): |
| output = Variable(self.data.new(self.data.size(0), matrix.data.size(1))) |
| return self._static_blas(Addmm, (output, 0, 1, self, matrix), False) |
| |
| def bmm(self, batch): |
| output = Variable(self.data.new(self.data.size(0), self.data.size(1), |
| batch.data.size(2))) |
| return self._static_blas(Baddbmm, (output, 0, 1, self, batch), False) |
| |
| def mv(self, vector): |
| output = Variable(self.data.new(self.data.size(0))) |
| return self._static_blas(Addmv, (output, 0, 1, self, vector), False) |
| |
| def ger(self, vector): |
| output = Variable(self.data.new(self.data.size(0), vector.data.size(0))) |
| return self._static_blas(Addr, (output, 0, 1, self, vector), False) |
| |
| def resize(self, *sizes): |
| return Resize(*sizes)(self) |
| |
| def resize_as(self, variable): |
| return Resize(*variable.size())(self) |
| |
| def addmm(self, *args): |
| return self._blas(Addmm, args, False) |
| |
| def addmm_(self, *args): |
| return self._blas(Addmm, args, True) |
| |
| def addbmm(self, *args): |
| return self._blas(Addbmm, args, False) |
| |
| def addbmm_(self, *args): |
| return self._blas(Addbmm, args, True) |
| |
| def baddbmm(self, *args): |
| return self._blas(Baddbmm, args, False) |
| |
| def baddbmm_(self, *args): |
| return self._blas(Baddbmm, args, True) |
| |
| def addmv(self, *args): |
| return self._blas(Addmv, args, False) |
| |
| def addmv_(self, *args): |
| return self._blas(Addmv, args, True) |
| |
| def addr(self, *args): |
| return self._blas(Addr, args, False) |
| |
| def addr_(self, *args): |
| return self._blas(Addr, args, True) |
| |
| def dot(self, other): |
| return Dot()(self, other) |
| |
| def _addcop(self, op, args): |
| if len(args) == 3: |
| # scale, tensor1, tensor2 |
| return op(args[0])(self, *args[1:]) |
| else: |
| # tensor1, tensor2 |
| return op()(self, *args) |
| |
| def addcmul(self, *args): |
| return self._addcop(Addcmul, args) |
| |
| def addcdiv(self, *args): |
| return self._addcop(Addcdiv, args) |
| |
| def norm(self, norm_type=2, dim=None): |
| return Norm(norm_type, dim)(self) |
| |
| def dist(self, tensor, norm_type=2): |
| return Norm(norm_type)(self - tensor) |
| |
| def index_add(self, dim, index, tensor): |
| return IndexAdd(dim)(self, index, tensor) |
| |
| def index_add_(self, dim, index, tensor): |
| return IndexAdd(dim, True)(self, index, tensor) |
| |
| def index_copy(self, dim, index, tensor): |
| return IndexCopy(dim)(self, index, tensor) |
| |
| def index_copy_(self, dim, index, tensor): |
| return IndexCopy(dim, True)(self, index, tensor) |
| |
| def index_fill(self, dim, index, value): |
| return IndexFill(dim, value)(self, index) |
| |
| def index_fill_(self, dim, index, value): |
| return IndexFill(dim, value, True)(self, index) |
| |
| def index_select(self, dim, index): |
| return IndexSelect(dim)(self, index) |
| |
| def gather(self, dim, index): |
| return Gather(dim)(self, index) |
| |
| def scatter(self, dim, index, source): |
| return Scatter(dim)(self, index, source) |
| |
| def scatter_(self, dim, index, source): |
| return Scatter(dim, True)(self, index, source) |
| |
| def masked_copy(self, mask, variable): |
| return MaskedCopy()(self, mask, variable) |
| |
| def masked_copy_(self, mask, variable): |
| return MaskedCopy(True)(self, mask, variable) |
| |
| def masked_fill(self, mask, value): |
| return MaskedFill(value)(self, mask) |
| |
| def masked_fill_(self, mask, value): |
| return MaskedFill(value, True)(self, mask) |
| |
| def masked_select(self, mask): |
| return MaskedSelect()(self, mask) |
| |
| def expand(self, *sizes): |
| if isinstance(sizes[0], torch.Size): |
| if len(sizes) > 1: |
| raise ValueError("expand expects a several ints or a single " |
| "torch.Size argument") |
| sizes = sizes[0] |
| return Expand(sizes)(self) |
| |
| def expand_as(self, tensor): |
| return Expand(tensor.size())(self) |
| |
| def t(self): |
| return Transpose(0, 1)(self) |
| |
| def transpose(self, dim1, dim2): |
| return Transpose(dim1, dim2)(self) |
| |
| def select(self, dim, _index): |
| index = tuple(slice(None, None) for _ in range(dim)) + (_index,) |
| return Index(index)(self) |
| |
| def narrow(self, dim, start_index, length): |
| index = tuple(slice(None, None) for _ in range(dim)) + \ |
| (slice(start_index, start_index + length),) |
| |
| return Index(index)(self) |
| |
| def chunk(self, num_chunks, dim=0): |
| return Chunk(num_chunks, dim)(self) |
| |
| def squeeze(self, dim=None): |
| return Squeeze(dim)(self) |
| |
| def unsqueeze(self, dim): |
| return Unsqueeze(dim)(self) |
| |
| def permute(self, *permutation): |
| return Permute(permutation)(self) |
| |
| def diag(self, diagonal_idx=0): |
| return Diag(diagonal_idx)(self) |
| |
| def tril(self, diagonal_idx=0): |
| return Tril(diagonal_idx)(self) |
| |
| def triu(self, diagonal_idx=0): |
| return Triu(diagonal_idx)(self) |
| |
| def multinomial(self, num_samples=1, with_replacement=False): |
| return Multinomial(num_samples, with_replacement)(self) |
| |
| def bernoulli(self): |
| return Bernoulli()(self) |
| |
| def eq(self, other): |
| if isinstance(other, Variable): |
| return Eq()(self, other) |
| assert not torch.is_tensor(other), "can't compare Variable and tensor" |
| return Eq(other)(self) |
| |
| def ne(self, other): |
| if isinstance(other, Variable): |
| return Ne()(self, other) |
| assert not torch.is_tensor(other), "can't compare Variable and tensor" |
| return Ne(other)(self) |
| |
| def gt(self, other): |
| if isinstance(other, Variable): |
| return Gt()(self, other) |
| assert not torch.is_tensor(other), "can't compare Variable and tensor" |
| return Gt(other)(self) |
| |
| def ge(self, other): |
| if isinstance(other, Variable): |
| return Ge()(self, other) |
| assert not torch.is_tensor(other), "can't compare Variable and tensor" |
| return Ge(other)(self) |
| |
| def lt(self, other): |
| if isinstance(other, Variable): |
| return Lt()(self, other) |
| assert not torch.is_tensor(other), "can't compare Variable and tensor" |
| return Lt(other)(self) |
| |
| def le(self, other): |
| if isinstance(other, Variable): |
| return Le()(self, other) |
| assert not torch.is_tensor(other), "can't compare Variable and tensor" |
| return Le(other)(self) |
| |
| def __add__(self, other): |
| return self.add(other) |
| __radd__ = __add__ |
| |
| def __iadd__(self, other): |
| return self.add_(other) |
| |
| def __sub__(self, other): |
| return self.sub(other) |
| |
| def __isub__(self, other): |
| return self.sub_(other) |
| |
| def __rsub__(self, other): |
| return SubConstant(other, sub_tensor=True)(self) |
| |
| def __mul__(self, other): |
| return self.mul(other) |
| __rmul__ = __mul__ |
| |
| def __imul__(self, other): |
| return self.mul_(other) |
| |
| def __matmul__(self, other): |
| dim_self = self.dim() |
| try: |
| dim_other = other.dim() |
| except AttributeError: # not a Variable |
| return NotImplemented |
| if dim_self == 1 and dim_other == 1: |
| return self.dot(other) |
| if dim_self == 2 and dim_other == 1: |
| return self.mv(other) |
| if dim_self == 1 and dim_other == 2: |
| return self.unsqueeze(0).mm(other).squeeze(0) |
| elif dim_self == 2 and dim_other == 2: |
| return self.mm(other) |
| raise ValueError("both arguments to __matmul__ need to be 1D or 2D, " |
| "but they are {}D and {}D".format(dim_self, dim_other)) |
| |
| def __div__(self, other): |
| return self.div(other) |
| __truediv__ = __div__ |
| |
| def __rdiv__(self, other): |
| return DivConstant(other, div_by_tensor=True)(self) |
| __rtruediv__ = __rdiv__ |
| |
| def __idiv__(self, other): |
| return self.div_(other) |
| |
| def __pow__(self, other): |
| return self.pow(other) |
| |
| def __ipow__(self, other): |
| raise NotImplementedError("in-place pow not implemented") |
| |
| def __rpow__(self, other): |
| return PowConstant(other, tensor_power=True)(self) |
| |
| def __neg__(self): |
| return Negate()(self) |
| |
| def __len__(self): |
| return len(self.data) |
| |
| def __iter__(self): |
| return iter(map(lambda i: self[i], range(self.size(0)))) |
| |
| def __mod__(self, other): |
| return self.remainder(other) |
| |
| def __eq__(self, other): |
| return self.eq(other) |
| |
| def __ne__(self, other): |
| return self.ne(other) |
| |
| def __lt__(self, other): |
| return self.lt(other) |
| |
| def __le__(self, other): |
| return self.le(other) |
| |
| def __gt__(self, other): |
| return self.gt(other) |
| |
| def __ge__(self, other): |
| return self.ge(other) |
| |
| def __hash__(self): |
| return id(self) |
| |
| class _torch(object): |
| |
| @staticmethod |
| def cat(iterable, dim=0): |
| return Concat(dim)(*iterable) |
| |
| @staticmethod |
| def normal(means, std=1): |
| if isinstance(std, Variable): |
| return Normal()(means, std) |
| else: |
| return Normal(std)(means) |
| |
| @staticmethod |
| def _blas(cls, args, inplace): |
| num_args = len(args) |
| alpha = beta = 1 |
| if num_args > 5: |
| raise RuntimeError("too many args") |
| if num_args == 5: |
| alpha, beta = args[0], args[2] |
| tensors = args[1:2] + args[3:] |
| elif num_args == 4: |
| alpha = args[0] |
| tensors = args[1:] |
| else: |
| tensors = args |
| return cls(alpha, beta, inplace)(*tensors) |
| |
| @classmethod |
| def addmm(cls, *args): |
| return cls._blas(Addmm, args, False) |
| |
| @classmethod |
| def addbmm(cls, *args): |
| return cls._blas(Addbmm, args, False) |
| |
| @classmethod |
| def baddbmm(cls, *args): |
| return cls._blas(Baddbmm, args, False) |
| |
| @classmethod |
| def addmv(cls, *args): |
| return cls._blas(Addmv, args, False) |
| |
| @classmethod |
| def addr(cls, *args): |
| return cls._blas(Addr, args, False) |
| |
| |
| for method in dir(Variable): |
| # This will also wrap some methods that normally aren't part of the |
| # funcitonal interface, but we don't care, as they won't ever be used |
| if method.startswith('_') or method.endswith('_'): |
| continue |
| if hasattr(Variable._torch, method): |
| continue |
| as_static = staticmethod(getattr(Variable, method)) |
| setattr(Variable._torch, method, as_static) |
| |
| |
| from .engine import ImperativeEngine |
| Variable._execution_engine = ImperativeEngine() |
