# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import numpy as np import warnings import weakref import sys import paddle from .. import framework from ..framework import convert_np_dtype_to_dtype_, _in_legacy_dygraph from .. import core from .. import unique_name from ..framework import Variable, Parameter, ParamBase, _getitem_impl_, _setitem_impl_, EagerParamBase, in_dygraph_mode from .base import switch_to_static_graph from .math_op_patch import monkey_patch_math_varbase from .parallel import scale_loss from paddle.fluid.data_feeder import convert_dtype, _PADDLE_DTYPE_2_NUMPY_DTYPE import paddle.utils.deprecated as deprecated import paddle.profiler as profiler from paddle.profiler.utils import in_profiler_mode from paddle import _C_ops, _legacy_C_ops _grad_scalar = None class TensorHookRemoveHelper(object): """ A helper class that for removing Tensor gradient's hook. NOTE(wuweilong):the operation weakref.ref(tensor) will cause some unexpected errors in eager mode. """ def __init__(self, tensor, hook_id): self._tensor = tensor if framework._in_eager_mode_ else weakref.ref( tensor) self._hook_id = hook_id def remove(self): """ Remove reference Tensor's hook. Returns: bool: Return True if removed successfully """ tensor = self._tensor if framework._in_eager_mode_ else self._tensor() if tensor is not None: res = tensor._remove_grad_hook(self._hook_id) if res is True: return True else: warnings.warn( "The backward hook (ID: %d) of Tensor `%s` you want to remove does not exist or has been removed." % (self._hook_id, tensor.name), RuntimeWarning) return False _already_patch_repr = False def monkey_patch_varbase(): @switch_to_static_graph def _to_static_var(self, to_parameter=False, **kwargs): """ **Notes**: **This API is ONLY available in Dygraph mode** Transform a VarBase into static Variable with same attributes. It's a low level interface used in dy2static and shall not be called directly. Args: to_parameter (bool): It takes effect only if the input a VarBase. If set True, the VarBase will be converted into framework.Parameters. Otherwise, it will be converted into framework.Variable. Default False. Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable import numpy as np data = np.ones([3, 1024], dtype='float32') with fluid.dygraph.guard(): var_base = to_variable(data) 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 that different between dynamic and static graph, should not getattr(self, attr, None). attr_not_need_keys = ['grad', 'T', 'place', '_place_str'] param_keys = ['stop_gradient', 'trainable'] if isinstance(self, (ParamBase, EagerParamBase)): attr_kwargs = self.__dict__.copy() for key in param_keys: attr_kwargs[key] = getattr(self, key) else: attr_names = [] for name in dir(self): if name not in attr_not_need_keys: if not inspect.ismethod(getattr( self, name)) and not 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'] for attr in attr_keys: attr_kwargs[attr] = getattr(self, attr, None) # If specify block, use it instead of self.block if 'block' in kwargs: attr_kwargs['block'] = kwargs['block'] attr_kwargs.update(kwargs) if to_parameter or isinstance(self, (ParamBase, EagerParamBase)): del attr_kwargs['persistable'] # NOTE(Aurelius84): All parameters should be placed into global block. attr_kwargs['block'] = attr_kwargs['block'].program.global_block() static_var = Parameter(**attr_kwargs) else: static_var = Variable(**attr_kwargs) return static_var # TODO(jiabin): move this to cplusplus end if we find some performance issue on it @framework.dygraph_only def set_value(self, value): """ **Notes**: **This API is ONLY available in Dygraph mode** Set a new value for this Variable. Args: value (Variable|np.ndarray): the new value. Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph import Linear import numpy as np data = np.ones([3, 1024], dtype='float32') with fluid.dygraph.guard(): linear = fluid.dygraph.Linear(1024, 4) t = to_variable(data) linear(t) # call with default weight custom_weight = np.random.randn(1024, 4).astype("float32") linear.weight.set_value(custom_weight) # change existing weight out = linear(t) # call with different weight """ if framework._in_eager_mode_: base_tensor = core.eager.Tensor else: base_tensor = core.VarBase assert isinstance(value, (np.ndarray, base_tensor, dict, str)), \ "Variable set_value function, arguments type only support Variable, numpy, VarBase, dict, string." if isinstance(value, (dict, str)): assert len(self) == len( value ), "Variable length not match, Variable [ {} ] need tensor with length {} but load set tensor with length {}".format( self.name, len(self), len(value)) if isinstance(value, dict): self.value().set_vocab(value) else: self.value().set_string_list(value) else: assert self.shape == list(value.shape), \ "Variable Shape not match, Variable [ {} ] need tensor with shape {} but load set tensor with shape {}".format( self.name, self.shape, value.shape) if isinstance(value, base_tensor): dtype = value.dtype else: dtype = convert_np_dtype_to_dtype_(value.dtype) assert self.dtype == dtype, \ "Variable dtype not match, Variable [ {} ] need tensor with dtype {} but load tensor with dtype {}".format( self.name, self.dtype, dtype) # NOTE(wuweilong): self could be VarBase or Tensor, the subsequent behavior are defined in different files # if self is VarBase, method value() return Variable that bindded in imperative.cc, get_tensor() bindded in pybind.cc # if self is Tensor, method value() return self that defined in this file, get_tensor() defined in eager_method.cc # this Interface behavior will be unifed in the future. self.value().get_tensor().set(value, framework._current_expected_place()) @framework.dygraph_only def backward(self, grad_tensor=None, retain_graph=False): """ Run backward of current Graph which starts from current Tensor. The new gradient will accumulat on previous gradient. You can clear gradient by ``Tensor.clear_grad()`` . Args: grad_tensor(Tensor, optional): initial gradient values of the current Tensor. If `grad_tensor` is None, the initial gradient values of the current Tensor would be Tensor filled with 1.0; if `grad_tensor` is not None, it must have the same length as the current Tensor. Teh default value is None. retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would like to add more ops to the built graph after calling this method( :code:`backward` ), set the parameter :code:`retain_graph` to True, then the grads will be retained. Thus, seting it to False is much more memory-efficient. Defaults to False. Returns: NoneType: None Examples: .. code-block:: python import paddle x = paddle.to_tensor(5., stop_gradient=False) for i in range(5): y = paddle.pow(x, 4.0) y.backward() print("{}: {}".format(i, x.grad)) # 0: [500.] # 1: [1000.] # 2: [1500.] # 3: [2000.] # 4: [2500.] x.clear_grad() print("{}".format(x.grad)) # 0. grad_tensor=paddle.to_tensor(2.) for i in range(5): y = paddle.pow(x, 4.0) y.backward(grad_tensor) print("{}: {}".format(i, x.grad)) # 0: [1000.] # 1: [2000.] # 2: [3000.] # 3: [4000.] # 4: [5000.] """ if framework._non_static_mode(): if in_profiler_mode(): record_event = profiler.RecordEvent( "Gradient Backward", profiler.TracerEventType.Backward) record_event.begin() if grad_tensor is not None: if framework._in_eager_mode_: assert isinstance( grad_tensor, core.eager.Tensor ), "The type of grad_tensor must be paddle.Tensor" else: assert isinstance( grad_tensor, paddle.Tensor ), "The type of grad_tensor must be paddle.Tensor" assert grad_tensor.shape == self.shape, \ "Tensor shape not match, Tensor of grad_tensor [ {} ] with shape {} mismatch Tensor [ {} ] with shape {}".format( grad_tensor.name, grad_tensor.shape, self.name, self.shape) if framework._in_eager_mode_: if grad_tensor is None: grad_tensor = [] else: grad_tensor = [grad_tensor] if _grad_scalar: # When using amp with Fleet DistributedStrategy, we do loss scaling implicitly. self = _grad_scalar.scale(self) if paddle.is_compiled_with_xpu() or paddle.is_compiled_with_npu( ) or paddle.is_compiled_with_mlu(): # TODO(liuyuhui): Currently only for xpu. Will be removed in the future. scaled_loss = scale_loss(self) if framework._in_eager_mode_: core.eager.run_backward([scaled_loss], grad_tensor, retain_graph) else: core.dygraph_run_backward([scaled_loss], [grad_tensor], retain_graph, framework._dygraph_tracer()) else: if framework._in_eager_mode_: core.eager.run_backward([self], grad_tensor, retain_graph) else: core.dygraph_run_backward([self], [grad_tensor], retain_graph, framework._dygraph_tracer()) if in_profiler_mode(): record_event.end() else: raise ValueError( "Variable.backward() is only available in DyGraph mode") @framework.dygraph_only @deprecated( since="2.1.0", level=1, reason= "Please use tensor.grad, which returns the tensor value of the gradient." ) def gradient(self): """ .. warning:: This API will be deprecated in the future, it is recommended to use :code:`x.grad` which returns the tensor value of the gradient. Get the Gradient of Current Tensor. Returns: ndarray: Numpy value of the gradient of current Tensor Examples: .. code-block:: python import paddle x = paddle.to_tensor(5., stop_gradient=False) y = paddle.pow(x, 4.0) y.backward() print("grad of x: {}".format(x.gradient())) # [500.] """ if framework._in_eager_mode_: if self.grad is None: return None if self.grad.is_selected_rows(): return (np.array(self.grad.numpy()), np.array(self.grad.rows())) return self.grad.numpy() else: if self._grad_ivar() is None: return None new_ivar = self._grad_ivar()._copy_to(core.CPUPlace(), True) if self._grad_ivar().type == core.VarDesc.VarType.SELECTED_ROWS: return (np.array( new_ivar.value().get_selected_rows().get_tensor()), np.array(new_ivar.value().get_selected_rows().rows())) else: return np.array(new_ivar.value().get_tensor()) @framework.dygraph_only def register_hook(self, hook): """ Registers a backward hook for current Tensor. The hook will be called every time the gradient Tensor of current Tensor is computed. The hook should not modify the input gradient Tensor, but it can optionally return a new gradient Tensor which will be used in place of current Tensor's gradient. The hook should have the following signature: hook(grad) -> Tensor or None Args: hook(function): A backward hook to be registered for Tensor.grad Returns: TensorHookRemoveHelper: A helper object that can be used to remove the registered hook by calling `remove()` method. Examples: .. code-block:: python import paddle # hook function return None def print_hook_fn(grad): print(grad) # hook function return Tensor def double_hook_fn(grad): grad = grad * 2 return grad x = paddle.to_tensor([0., 1., 2., 3.], stop_gradient=False) y = paddle.to_tensor([4., 5., 6., 7.], stop_gradient=False) z = paddle.to_tensor([1., 2., 3., 4.]) # one Tensor can register multiple hooks h = x.register_hook(print_hook_fn) x.register_hook(double_hook_fn) w = x + y # register hook by lambda function w.register_hook(lambda grad: grad * 2) o = z.matmul(w) o.backward() # print_hook_fn print content in backward # Tensor(shape=[4], dtype=float32, place=CUDAPlace(0), stop_gradient=False, # [2., 4., 6., 8.]) print("w.grad:", w.grad) # w.grad: [1. 2. 3. 4.] print("x.grad:", x.grad) # x.grad: [ 4. 8. 12. 16.] print("y.grad:", y.grad) # y.grad: [2. 4. 6. 8.] # remove hook h.remove() """ if self.stop_gradient is True: raise RuntimeError( "Cannot register hook on a tensor that stop gradient.") hook_id = self._register_grad_hook(hook) helper = TensorHookRemoveHelper(self, hook_id) return helper @framework.dygraph_only def _to(self, device=None, dtype=None, blocking=None): if device is None and dtype is None and blocking is None: return self if device is not None: if isinstance(device, str): device = paddle.device._convert_to_place(device) elif isinstance( device, (core.CPUPlace, core.CUDAPlace, core.CUDAPinnedPlace, core.XPUPlace, core.CustomPlace)): pass else: raise ValueError( "device value error, must be str, paddle.CPUPlace(), paddle.CUDAPlace(), paddle.CUDAPinnedPlace(), paddle.XPUPlace() or paddle.CustomPlace(), but the type of device is " + type(device).__name__) if blocking is None: blocking = True else: assert isinstance( blocking, bool), "blocking value error, must be the True, False or None" def transform(t, device, dtype, blocking): if device is None: device = t.place if dtype is None: dtype = t.dtype if type(dtype) is str: dtype = framework.convert_np_dtype_to_dtype_(dtype) # 1. gpu place need to determine whether the memory is sufficient for allocation. if t.place.is_gpu_place(): size_dtype = core.size_of_dtype(dtype) # Note(weilong wu): Paddle GPU minimum memory allocation unit is 256 bytes, # waiting_alloc_memory will compute the memory space occupied by 't'. # Coefficient 1.2 is used to avoid OOM that may occur in this critical state when the memory is just enough. waiting_alloc_memory = ( (t._numel() * size_dtype) / 256 + 1) * 256 * 1.2 gpu_memory_available = core.gpu_memory_available() if gpu_memory_available < waiting_alloc_memory: # Copy Tensor to cpu t_used = t._copy_to(paddle.CPUPlace(), blocking) # Release memory of t t._clear() else: # Tensor still in GPU t_used = t else: t_used = t # 2. cast Tensor to dtype if dtype is not None and dtype != t_used.dtype: with paddle.fluid.framework._dygraph_place_guard( place=t_used.place): t_casted = t_used.cast(dtype=dtype) else: t_casted = t_used # 3. Copy casted Tensor(in CPU or GPU) to device if device is not None and not t_casted.place._equals(device): new_t = t_casted._copy_to(device, blocking) else: new_t = t_casted # 4. Share Tensor to origin Tensor dst_tensor = t.value().get_tensor() src_tensor = new_t.value().get_tensor() dst_tensor._share_data_with(src_tensor) return t with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=UserWarning) return transform(self, device, dtype, blocking) @property def grad(self): """ .. warning:: This API will return the tensor value of the gradient. If you want to get the numpy value of the gradient, you can use :code:`x.grad.numpy()`. Get the Gradient of Current Tensor. Returns: Tensor: the gradient of current Tensor Examples: .. code-block:: python import paddle x = paddle.to_tensor(5., stop_gradient=False) y = paddle.pow(x, 4.0) y.backward() print("grad of x: {}".format(x.grad)) # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False, [500.]) """ msg = 'tensor.grad will return the tensor value of the gradient.' \ ' This is an incompatible upgrade for tensor.grad API. ' \ ' It\'s return type changes from numpy.ndarray in version 2.0 to paddle.Tensor in version 2.1.0. ' \ ' If you want to get the numpy value of the gradient, you can use :code:`x.grad.numpy()`' warning_msg = "\033[93m\nWarning:\n%s \033[0m" % (msg) # ensure ANSI escape sequences print correctly in cmd and powershell if sys.platform.lower() == 'win32': warning_msg = "\nWarning:\n%s " % (msg) warnings.warn(warning_msg) return self._grad_ivar() def clear_grad(self): """ The alias of clear_gradient(). """ self.clear_gradient() def item(self, *args): """ Convert element at specific position in Tensor into Python scalars. If the position is not specified, the Tensor must be a single-element Tensor. Args: *args(int): The input coordinates. If it's single int, the data in the corresponding order of flattened Tensor will be returned. Default: None, and it must be in the case where Tensor has only one element. Returns(Python scalar): A Python scalar, whose dtype is corresponds to the dtype of Tensor. Raises: ValueError: If the Tensor has more than one element, there must be coordinates. Examples: .. code-block:: python import paddle x = paddle.to_tensor(1) print(x.item()) #1 print(type(x.item())) # x = paddle.to_tensor(1.0) print(x.item()) #1.0 print(type(x.item())) # x = paddle.to_tensor(True) print(x.item()) #True print(type(x.item())) # x = paddle.to_tensor(1+1j) print(x.item()) #(1+1j) print(type(x.item())) # x = paddle.to_tensor([[1.1, 2.2, 3.3]]) print(x.item(2)) #3.3 print(x.item(0, 2)) #3.3 """ return self._getitem_from_offset(*args).item() @property def inplace_version(self): """ The inplace version of current Tensor. The version number is incremented whenever the current Tensor is modified through an inplace operation. **Notes: This is a read-only property** Examples: .. code-block:: python import paddle var = paddle.ones(shape=[4, 2, 3], dtype="float32") print(var.inplace_version) # 0 var[1] = 2.2 print(var.inplace_version) # 1 """ return self._inplace_version() def __str__(self): """ Convert a VarBase object to a readable string. Returns(str): A readable string. Examples: .. code-block:: python import paddle x = paddle.rand([2, 5]) print(x) # Tensor(shape=[2, 5], dtype=float32, place=CPUPlace, # [[0.30574632, 0.55739117, 0.30902600, 0.39413780, 0.44830436], # [0.79010487, 0.53972793, 0.09495186, 0.44267157, 0.72112119]]) """ if framework._in_eager_mode_: from paddle.tensor.to_string import tensor_to_string return tensor_to_string(self) else: from paddle.tensor.to_string import to_string return to_string(self) def __deepcopy__(self, memo): """ Deep copy Tensor, it will always performs Tensor copy. Examples: .. code-block:: python import paddle import copy x = paddle.to_tensor(2.) y = copy.deepcopy(x) print(x) # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True, # [2.]) print(y) # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True, # [2.]) """ if not self.is_leaf: raise RuntimeError( "Only Leaf Tensor support the deepcopy at the moment, non-Leaf Tensors contains graph information that does't support deepcopy" ) if framework._in_eager_mode_: new_varbase = core.eager.Tensor() else: new_varbase = core.VarBase() new_varbase.name = self.name + unique_name.generate("_deepcopy") memo[id(self)] = new_varbase new_varbase.copy_(self, True) return new_varbase @property def block(self): return framework.default_main_program().global_block() def __nonzero__(self): numel = np.prod(self.shape) assert numel == 1, "When Variable is used as the condition of if/while , Variable can only contain one element." if framework._in_eager_mode_: assert self._is_initialized(), "tensor not initialized" return bool(np.all(self.numpy() > 0)) else: tensor = self.value().get_tensor() assert tensor._is_initialized(), "tensor not initialized" return bool(np.all(tensor.__array__() > 0)) def __bool__(self): return self.__nonzero__() def __array__(self, dtype=None): """ Returns a numpy array shows the value of current Tensor. Returns: ndarray: The numpy value of current Tensor. Returns type: ndarray: dtype is same as current Tensor Examples: .. code-block:: python import paddle import numpy as np x = paddle.randn([2, 2]) x_array = np.array(x) print(type(x_array)) # print(x_array.shape) #(2, 2) """ array = self.numpy() if dtype: array = array.astype(dtype) return array def contain_tensor(item): if not isinstance(item, (tuple, list)): item = [item] for slice_item in item: if isinstance(slice_item, slice): if isinstance(slice_item.start, Variable) \ or isinstance(slice_item.stop, Variable) \ or isinstance(slice_item.step, Variable): return True else: if isinstance( slice_item, (Variable, np.ndarray)) and Variable.dtype != paddle.bool: return True return False def __getitem__(self, item): def is_list_tuple(index, contain_type): def _is_list_tuple(item): if isinstance(item, (tuple, list)): for s in item: if not _is_list_tuple(s): return False else: if type(item) != contain_type: return False return True if not isinstance(index, (tuple, list)): return False for s in index: if not _is_list_tuple(s): return False return True if contain_tensor(item) or is_list_tuple(item, int): # 1. Call _getitem_impl_ when item contains tensor. # Why not call a c++ function ? Because item can't be parsed when it contains tensor. return _getitem_impl_(self, item) else: # 2. Call c++ func getitem_index_not_tensor to speedup. return self._getitem_index_not_tensor(item) def __setitem__(self, item, value): def contain_tensor_or_list(item): if not isinstance(item, tuple): item = [item] for slice_item in item: if isinstance(slice_item, list): return True elif isinstance(slice_item, Variable): return True return False def is_combine_index(item): var_type = None item_type = None if isinstance(item, (tuple, list)): for slice_item in item: if item_type is None: item_type = type(slice_item) else: if type(slice_item) != item_type: return True if isinstance(slice_item, Variable): if var_type is None: var_type = slice_item.dtype else: if var_type != slice_item.dtype: return True return False return False if contain_tensor_or_list(item) and not is_combine_index(item): # To reuse code with static graph, # Call _setitem_impl_ when item contains tensor or list. return _setitem_impl_(self, item, value) else: if framework._in_eager_mode_: return self.__setitem_eager_tensor__(item, value) else: # Call c++ func __setitem_varbase__ to speedup. return self.__setitem_varbase__(item, value) @framework.dygraph_only def _grad_ivar(self): if self.grad is not None: if self.grad._is_initialized(): return self.grad return None @framework.dygraph_only def _set_grad_ivar(self, value): if isinstance(self, EagerParamBase): self.grad = value self._unset_fake_empty() else: raise TypeError( "_set_grad_ivar is only supported for Parameter Tensor") @framework.dygraph_only def clone(self): if in_dygraph_mode(): return _C_ops.assign(self) if _in_legacy_dygraph(): output = core.VarBase() else: output = core.eager.Tensor() return _legacy_C_ops.assign(self, output) @framework.dygraph_only def value(self): return self @framework.dygraph_only def _slice(self, begin_idx, end_idx): return core.eager.Tensor(self.get_tensor()._slice(begin_idx, end_idx)) @framework.dygraph_only def _numel(self): return self.get_tensor()._numel() @framework.dygraph_only def _clear_data(self): self.get_tensor()._clear() @framework.dygraph_only def _uva(self, device_id=0): ''' Returns self tensor with the UVA(unified virtual addressing). Args: device_id(int, optional): The destination GPU device id. Default: None, means current device. Examples: .. code-block:: python # required: gpu import paddle x = paddle.to_tensor([1, 2, 3], place=paddle.CPUPlace()) x._uva() print(x) ''' self._tensor_uva(device_id) @framework.dygraph_only def cpu(self): if self.place.is_cpu_place(): return self else: res = self._copy_to(core.CPUPlace(), True) res.stop_gradient = self.stop_gradient res.persistable = self.persistable return res @framework.dygraph_only def cuda(self, device_id=None, blocking=True): if device_id is None: res_place = framework._current_expected_place() if not isinstance(res_place, core.CUDAPlace): res_place = core.CUDAPlace(0) elif isinstance(device_id, int): res_place = core.CUDAPlace(device_id) else: raise ValueError("device_id must be int|None") if self.place._equals(res_place): return self else: res = self._copy_to(res_place, True) res.stop_gradient = self.stop_gradient res.persistable = self.persistable return res @framework.dygraph_only def pin_memory(self): if self.place.is_cuda_pinned_place(): return self else: res = self._copy_to(core.CUDAPinnedPlace(), True) res.stop_gradient = self.stop_gradient res.persistable = self.persistable return res @framework.dygraph_only def values(self): """ **Notes**: **This API is ONLY available in Dygraph mode** Get the values of current SparseTensor(COO or CSR). Returns: Tensor: A DenseTensor Examples: .. code-block:: python import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] values = [1, 2, 3, 4, 5] dense_shape = [3, 4] sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape) print(sparse_x.values()) #[1, 2, 3, 4, 5] """ return _C_ops.sparse_values(self) @framework.dygraph_only def to_dense(self): """ **Notes**: **This API is ONLY available in Dygraph mode** Convert the current SparseTensor(COO or CSR) to DenseTensor. Returns: Tensor: A DenseTensor Examples: .. code-block:: python import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] values = [1, 2, 3, 4, 5] dense_shape = [3, 4] sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int64'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape) dense_x = sparse_x.to_dense() #[[0., 1., 0., 2.], # [0., 0., 3., 0.], # [4., 5., 0., 0.]] """ return _C_ops.sparse_to_dense(self) @framework.dygraph_only def to_sparse_coo(self, sparse_dim): """ **Notes**: **This API is ONLY available in Dygraph mode** Convert the current DenseTensor to SparseTensor in COO format. Returns: Tensor: A SparseCooTensor Examples: .. code-block:: python import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): dense_x = [[0, 1, 0, 2], [0, 0, 3, 4]] dense_x = paddle.to_tensor(dense_x, dtype='float32') sparse_x = dense_x.to_sparse_coo(sparse_dim=2) #indices=[[0, 0, 1, 1], # [1, 3, 2, 3]], #values=[1., 2., 3., 4.] """ return _C_ops.sparse_to_sparse_coo(self, sparse_dim) if framework._in_eager_mode_ and not hasattr(core, "eager"): return for method_name, method in (("__bool__", __bool__), ("__nonzero__", __nonzero__), ("_to_static_var", _to_static_var), ("set_value", set_value), ("block", block), ("backward", backward), ("clear_grad", clear_grad), ("inplace_version", inplace_version), ("gradient", gradient), ("register_hook", register_hook), ("__str__", __str__), ("__repr__", __str__), ("__deepcopy__", __deepcopy__), ("__module__", "paddle"), ("__array__", __array__), ("__getitem__", __getitem__), ("item", item), ("__setitem__", __setitem__), ("_to", _to), ("values", values), ("to_dense", to_dense), ("to_sparse_coo", to_sparse_coo)): if framework._in_eager_mode_: setattr(core.eager.Tensor, method_name, method) else: setattr(core.VarBase, method_name, method) if framework._in_eager_mode_: setattr(core.eager.Tensor, "_grad_ivar", _grad_ivar) setattr(core.eager.Tensor, "_set_grad_ivar", _set_grad_ivar) setattr(core.eager.Tensor, "clone", clone) setattr(core.eager.Tensor, "value", value) setattr(core.eager.Tensor, "cpu", cpu) setattr(core.eager.Tensor, "cuda", cuda) setattr(core.eager.Tensor, "pin_memory", pin_memory) setattr(core.eager.Tensor, "_slice", _slice) setattr(core.eager.Tensor, "_numel", _numel) setattr(core.eager.Tensor, "_uva", _uva) setattr(core.eager.Tensor, "_clear_data", _clear_data) else: setattr(core.VarBase, "__name__", "Tensor") setattr(core.VarBase, "grad", grad) global _already_patch_repr if not _already_patch_repr: # NOTE(zhiqiu): pybind11 will set a default __str__ method of enum class. # So, we need to overwrite it to a more readable one. # See details in https://github.com/pybind/pybind11/issues/2537. origin = getattr(core.VarDesc.VarType, "__repr__") def dtype_str(dtype): if dtype in _PADDLE_DTYPE_2_NUMPY_DTYPE: numpy_dtype = _PADDLE_DTYPE_2_NUMPY_DTYPE[dtype] if numpy_dtype == 'uint16': numpy_dtype = 'bfloat16' prefix = 'paddle.' return prefix + numpy_dtype else: # for example, paddle.fluid.core.VarDesc.VarType.LOD_TENSOR return origin(dtype) setattr(core.VarDesc.VarType, "__repr__", dtype_str) _already_patch_repr = True # patch math methods for varbase monkey_patch_math_varbase()