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1481 lines
68 KiB
1481 lines
68 KiB
import math
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import warnings
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import numbers
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import weakref
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from typing import List, Tuple, Optional, overload
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import torch
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from torch import Tensor
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from .module import Module
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from ..parameter import Parameter
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from ..utils.rnn import PackedSequence
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from .. import init
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from ... import _VF
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__all__ = ['RNNBase', 'RNN', 'LSTM', 'GRU', 'RNNCellBase', 'RNNCell', 'LSTMCell', 'GRUCell']
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_rnn_impls = {
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'RNN_TANH': _VF.rnn_tanh,
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'RNN_RELU': _VF.rnn_relu,
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}
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def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
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return tensor.index_select(dim, permutation)
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def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
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warnings.warn("apply_permutation is deprecated, please use tensor.index_select(dim, permutation) instead")
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return _apply_permutation(tensor, permutation, dim)
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class RNNBase(Module):
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r"""Base class for RNN modules (RNN, LSTM, GRU).
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Implements aspects of RNNs shared by the RNN, LSTM, and GRU classes, such as module initialization
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and utility methods for parameter storage management.
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.. note::
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The forward method is not implemented by the RNNBase class.
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.. note::
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LSTM and GRU classes override some methods implemented by RNNBase.
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"""
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__constants__ = ['mode', 'input_size', 'hidden_size', 'num_layers', 'bias',
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'batch_first', 'dropout', 'bidirectional', 'proj_size']
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__jit_unused_properties__ = ['all_weights']
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mode: str
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input_size: int
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hidden_size: int
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num_layers: int
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bias: bool
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batch_first: bool
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dropout: float
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bidirectional: bool
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proj_size: int
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def __init__(self, mode: str, input_size: int, hidden_size: int,
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num_layers: int = 1, bias: bool = True, batch_first: bool = False,
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dropout: float = 0., bidirectional: bool = False, proj_size: int = 0,
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device=None, dtype=None) -> None:
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factory_kwargs = {'device': device, 'dtype': dtype}
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super().__init__()
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self.mode = mode
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.bias = bias
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self.batch_first = batch_first
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self.dropout = float(dropout)
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self.bidirectional = bidirectional
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self.proj_size = proj_size
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self._flat_weight_refs: List[Optional[weakref.ReferenceType[Parameter]]] = []
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num_directions = 2 if bidirectional else 1
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if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \
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isinstance(dropout, bool):
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raise ValueError("dropout should be a number in range [0, 1] "
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"representing the probability of an element being "
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"zeroed")
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if dropout > 0 and num_layers == 1:
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warnings.warn("dropout option adds dropout after all but last "
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"recurrent layer, so non-zero dropout expects "
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f"num_layers greater than 1, but got dropout={dropout} and "
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f"num_layers={num_layers}")
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if not isinstance(hidden_size, int):
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raise TypeError(f"hidden_size should be of type int, got: {type(hidden_size).__name__}")
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if hidden_size <= 0:
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raise ValueError("hidden_size must be greater than zero")
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if num_layers <= 0:
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raise ValueError("num_layers must be greater than zero")
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if proj_size < 0:
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raise ValueError("proj_size should be a positive integer or zero to disable projections")
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if proj_size >= hidden_size:
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raise ValueError("proj_size has to be smaller than hidden_size")
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if mode == 'LSTM':
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gate_size = 4 * hidden_size
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elif mode == 'GRU':
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gate_size = 3 * hidden_size
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elif mode == 'RNN_TANH':
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gate_size = hidden_size
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elif mode == 'RNN_RELU':
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gate_size = hidden_size
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else:
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raise ValueError("Unrecognized RNN mode: " + mode)
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self._flat_weights_names = []
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self._all_weights = []
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for layer in range(num_layers):
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for direction in range(num_directions):
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real_hidden_size = proj_size if proj_size > 0 else hidden_size
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layer_input_size = input_size if layer == 0 else real_hidden_size * num_directions
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w_ih = Parameter(torch.empty((gate_size, layer_input_size), **factory_kwargs))
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w_hh = Parameter(torch.empty((gate_size, real_hidden_size), **factory_kwargs))
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b_ih = Parameter(torch.empty(gate_size, **factory_kwargs))
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# Second bias vector included for CuDNN compatibility. Only one
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# bias vector is needed in standard definition.
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b_hh = Parameter(torch.empty(gate_size, **factory_kwargs))
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layer_params: Tuple[Tensor, ...] = ()
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if self.proj_size == 0:
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if bias:
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layer_params = (w_ih, w_hh, b_ih, b_hh)
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else:
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layer_params = (w_ih, w_hh)
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else:
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w_hr = Parameter(torch.empty((proj_size, hidden_size), **factory_kwargs))
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if bias:
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layer_params = (w_ih, w_hh, b_ih, b_hh, w_hr)
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else:
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layer_params = (w_ih, w_hh, w_hr)
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suffix = '_reverse' if direction == 1 else ''
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param_names = ['weight_ih_l{}{}', 'weight_hh_l{}{}']
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if bias:
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param_names += ['bias_ih_l{}{}', 'bias_hh_l{}{}']
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if self.proj_size > 0:
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param_names += ['weight_hr_l{}{}']
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param_names = [x.format(layer, suffix) for x in param_names]
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for name, param in zip(param_names, layer_params):
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setattr(self, name, param)
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self._flat_weights_names.extend(param_names)
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self._all_weights.append(param_names)
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self._init_flat_weights()
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self.reset_parameters()
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def _init_flat_weights(self):
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self._flat_weights = [getattr(self, wn) if hasattr(self, wn) else None
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for wn in self._flat_weights_names]
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self._flat_weight_refs = [weakref.ref(w) if w is not None else None
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for w in self._flat_weights]
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self.flatten_parameters()
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def __setattr__(self, attr, value):
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if hasattr(self, "_flat_weights_names") and attr in self._flat_weights_names:
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# keep self._flat_weights up to date if you do self.weight = ...
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idx = self._flat_weights_names.index(attr)
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self._flat_weights[idx] = value
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super().__setattr__(attr, value)
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def flatten_parameters(self) -> None:
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"""Reset parameter data pointer so that they can use faster code paths.
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Right now, this works only if the module is on the GPU and cuDNN is enabled.
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Otherwise, it's a no-op.
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"""
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# Short-circuits if _flat_weights is only partially instantiated
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if len(self._flat_weights) != len(self._flat_weights_names):
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return
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for w in self._flat_weights:
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if not isinstance(w, Tensor):
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return
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# Short-circuits if any tensor in self._flat_weights is not acceptable to cuDNN
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# or the tensors in _flat_weights are of different dtypes
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first_fw = self._flat_weights[0]
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dtype = first_fw.dtype
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for fw in self._flat_weights:
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if (not isinstance(fw.data, Tensor) or not (fw.data.dtype == dtype) or
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not fw.data.is_cuda or
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not torch.backends.cudnn.is_acceptable(fw.data)):
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return
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# If any parameters alias, we fall back to the slower, copying code path. This is
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# a sufficient check, because overlapping parameter buffers that don't completely
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# alias would break the assumptions of the uniqueness check in
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# Module.named_parameters().
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unique_data_ptrs = {p.data_ptr() for p in self._flat_weights}
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if len(unique_data_ptrs) != len(self._flat_weights):
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return
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with torch.cuda.device_of(first_fw):
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import torch.backends.cudnn.rnn as rnn
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# Note: no_grad() is necessary since _cudnn_rnn_flatten_weight is
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# an inplace operation on self._flat_weights
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with torch.no_grad():
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if torch._use_cudnn_rnn_flatten_weight():
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num_weights = 4 if self.bias else 2
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if self.proj_size > 0:
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num_weights += 1
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torch._cudnn_rnn_flatten_weight(
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self._flat_weights, num_weights,
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self.input_size, rnn.get_cudnn_mode(self.mode),
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self.hidden_size, self.proj_size, self.num_layers,
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self.batch_first, bool(self.bidirectional))
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def _apply(self, fn, recurse=True):
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self._flat_weight_refs = []
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ret = super()._apply(fn, recurse)
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# Resets _flat_weights
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# Note: be v. careful before removing this, as 3rd party device types
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# likely rely on this behavior to properly .to() modules like LSTM.
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self._init_flat_weights()
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return ret
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def reset_parameters(self) -> None:
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stdv = 1.0 / math.sqrt(self.hidden_size) if self.hidden_size > 0 else 0
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for weight in self.parameters():
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init.uniform_(weight, -stdv, stdv)
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def check_input(self, input: Tensor, batch_sizes: Optional[Tensor]) -> None:
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if not torch.jit.is_scripting():
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if input.dtype != self._flat_weights[0].dtype and not torch._C._is_any_autocast_enabled():
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raise ValueError(f'input must have the type {self._flat_weights[0].dtype}, got type {input.dtype}')
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expected_input_dim = 2 if batch_sizes is not None else 3
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if input.dim() != expected_input_dim:
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raise RuntimeError(
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f'input must have {expected_input_dim} dimensions, got {input.dim()}')
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if self.input_size != input.size(-1):
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raise RuntimeError(
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f'input.size(-1) must be equal to input_size. Expected {self.input_size}, got {input.size(-1)}')
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def get_expected_hidden_size(self, input: Tensor, batch_sizes: Optional[Tensor]) -> Tuple[int, int, int]:
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if batch_sizes is not None:
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mini_batch = int(batch_sizes[0])
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else:
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mini_batch = input.size(0) if self.batch_first else input.size(1)
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num_directions = 2 if self.bidirectional else 1
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if self.proj_size > 0:
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expected_hidden_size = (self.num_layers * num_directions,
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mini_batch, self.proj_size)
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else:
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expected_hidden_size = (self.num_layers * num_directions,
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mini_batch, self.hidden_size)
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return expected_hidden_size
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def check_hidden_size(self, hx: Tensor, expected_hidden_size: Tuple[int, int, int],
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msg: str = 'Expected hidden size {}, got {}') -> None:
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if hx.size() != expected_hidden_size:
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raise RuntimeError(msg.format(expected_hidden_size, list(hx.size())))
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def _weights_have_changed(self):
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# Returns True if the weight tensors have changed since the last forward pass.
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# This is the case when used with torch.func.functional_call(), for example.
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weights_changed = False
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for ref, name in zip(self._flat_weight_refs, self._flat_weights_names):
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weight = getattr(self, name) if hasattr(self, name) else None
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if weight is not None and ref is not None and ref() is not weight:
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weights_changed = True
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break
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return weights_changed
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def check_forward_args(self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]):
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self.check_input(input, batch_sizes)
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expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
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self.check_hidden_size(hidden, expected_hidden_size)
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def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]):
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if permutation is None:
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return hx
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return _apply_permutation(hx, permutation)
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def extra_repr(self) -> str:
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s = '{input_size}, {hidden_size}'
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if self.proj_size != 0:
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s += ', proj_size={proj_size}'
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if self.num_layers != 1:
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s += ', num_layers={num_layers}'
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if self.bias is not True:
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s += ', bias={bias}'
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if self.batch_first is not False:
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s += ', batch_first={batch_first}'
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if self.dropout != 0:
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s += ', dropout={dropout}'
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if self.bidirectional is not False:
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s += ', bidirectional={bidirectional}'
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return s.format(**self.__dict__)
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def _update_flat_weights(self):
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if not torch.jit.is_scripting():
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if self._weights_have_changed():
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self._init_flat_weights()
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def __getstate__(self):
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# If weights have been changed, update the _flat_weights in __getstate__ here.
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self._update_flat_weights()
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# Don't serialize the weight references.
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state = self.__dict__.copy()
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del state['_flat_weight_refs']
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return state
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def __setstate__(self, d):
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super().__setstate__(d)
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if 'all_weights' in d:
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self._all_weights = d['all_weights']
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# In PyTorch 1.8 we added a proj_size member variable to LSTM.
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# LSTMs that were serialized via torch.save(module) before PyTorch 1.8
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# don't have it, so to preserve compatibility we set proj_size here.
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if 'proj_size' not in d:
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self.proj_size = 0
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if not isinstance(self._all_weights[0][0], str):
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num_layers = self.num_layers
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num_directions = 2 if self.bidirectional else 1
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self._flat_weights_names = []
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self._all_weights = []
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for layer in range(num_layers):
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for direction in range(num_directions):
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suffix = '_reverse' if direction == 1 else ''
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weights = ['weight_ih_l{}{}', 'weight_hh_l{}{}', 'bias_ih_l{}{}',
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'bias_hh_l{}{}', 'weight_hr_l{}{}']
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weights = [x.format(layer, suffix) for x in weights]
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if self.bias:
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if self.proj_size > 0:
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self._all_weights += [weights]
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self._flat_weights_names.extend(weights)
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else:
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self._all_weights += [weights[:4]]
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self._flat_weights_names.extend(weights[:4])
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else:
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if self.proj_size > 0:
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self._all_weights += [weights[:2]] + [weights[-1:]]
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self._flat_weights_names.extend(weights[:2] + [weights[-1:]])
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else:
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self._all_weights += [weights[:2]]
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self._flat_weights_names.extend(weights[:2])
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self._flat_weights = [getattr(self, wn) if hasattr(self, wn) else None
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for wn in self._flat_weights_names]
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self._flat_weight_refs = [weakref.ref(w) if w is not None else None
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for w in self._flat_weights]
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@property
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def all_weights(self) -> List[List[Parameter]]:
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return [[getattr(self, weight) for weight in weights] for weights in self._all_weights]
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def _replicate_for_data_parallel(self):
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replica = super()._replicate_for_data_parallel()
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# Need to copy these caches, otherwise the replica will share the same
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# flat weights list.
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replica._flat_weights = replica._flat_weights[:]
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replica._flat_weights_names = replica._flat_weights_names[:]
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return replica
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class RNN(RNNBase):
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r"""__init__(input_size,hidden_size,num_layers=1,nonlinearity='tanh',bias=True,batch_first=False,dropout=0.0,bidirectional=False,device=None,dtype=None)
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Apply a multi-layer Elman RNN with :math:`\tanh` or :math:`\text{ReLU}`
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non-linearity to an input sequence. For each element in the input sequence,
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each layer computes the following function:
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.. math::
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h_t = \tanh(x_t W_{ih}^T + b_{ih} + h_{t-1}W_{hh}^T + b_{hh})
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where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is
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the input at time `t`, and :math:`h_{(t-1)}` is the hidden state of the
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previous layer at time `t-1` or the initial hidden state at time `0`.
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If :attr:`nonlinearity` is ``'relu'``, then :math:`\text{ReLU}` is used instead of :math:`\tanh`.
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.. code-block:: python
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# Efficient implementation equivalent to the following with bidirectional=False
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def forward(x, h_0=None):
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if batch_first:
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x = x.transpose(0, 1)
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seq_len, batch_size, _ = x.size()
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if h_0 is None:
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h_0 = torch.zeros(num_layers, batch_size, hidden_size)
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h_t_minus_1 = h_0
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h_t = h_0
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output = []
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for t in range(seq_len):
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for layer in range(num_layers):
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h_t[layer] = torch.tanh(
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x[t] @ weight_ih[layer].T
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+ bias_ih[layer]
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+ h_t_minus_1[layer] @ weight_hh[layer].T
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+ bias_hh[layer]
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)
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output.append(h_t[-1])
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h_t_minus_1 = h_t
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output = torch.stack(output)
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if batch_first:
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output = output.transpose(0, 1)
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return output, h_t
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Args:
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input_size: The number of expected features in the input `x`
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hidden_size: The number of features in the hidden state `h`
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num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
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would mean stacking two RNNs together to form a `stacked RNN`,
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with the second RNN taking in outputs of the first RNN and
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computing the final results. Default: 1
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nonlinearity: The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``
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bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
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Default: ``True``
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batch_first: If ``True``, then the input and output tensors are provided
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as `(batch, seq, feature)` instead of `(seq, batch, feature)`.
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Note that this does not apply to hidden or cell states. See the
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Inputs/Outputs sections below for details. Default: ``False``
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dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
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RNN layer except the last layer, with dropout probability equal to
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:attr:`dropout`. Default: 0
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bidirectional: If ``True``, becomes a bidirectional RNN. Default: ``False``
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Inputs: input, h_0
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* **input**: tensor of shape :math:`(L, H_{in})` for unbatched input,
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:math:`(L, N, H_{in})` when ``batch_first=False`` or
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:math:`(N, L, H_{in})` when ``batch_first=True`` containing the features of
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the input sequence. The input can also be a packed variable length sequence.
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See :func:`torch.nn.utils.rnn.pack_padded_sequence` or
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:func:`torch.nn.utils.rnn.pack_sequence` for details.
|
|
* **h_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
|
|
:math:`(D * \text{num\_layers}, N, H_{out})` containing the initial hidden
|
|
state for the input sequence batch. Defaults to zeros if not provided.
|
|
|
|
where:
|
|
|
|
.. math::
|
|
\begin{aligned}
|
|
N ={} & \text{batch size} \\
|
|
L ={} & \text{sequence length} \\
|
|
D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\
|
|
H_{in} ={} & \text{input\_size} \\
|
|
H_{out} ={} & \text{hidden\_size}
|
|
\end{aligned}
|
|
|
|
Outputs: output, h_n
|
|
* **output**: tensor of shape :math:`(L, D * H_{out})` for unbatched input,
|
|
:math:`(L, N, D * H_{out})` when ``batch_first=False`` or
|
|
:math:`(N, L, D * H_{out})` when ``batch_first=True`` containing the output features
|
|
`(h_t)` from the last layer of the RNN, for each `t`. If a
|
|
:class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output
|
|
will also be a packed sequence.
|
|
* **h_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
|
|
:math:`(D * \text{num\_layers}, N, H_{out})` containing the final hidden state
|
|
for each element in the batch.
|
|
|
|
Attributes:
|
|
weight_ih_l[k]: the learnable input-hidden weights of the k-th layer,
|
|
of shape `(hidden_size, input_size)` for `k = 0`. Otherwise, the shape is
|
|
`(hidden_size, num_directions * hidden_size)`
|
|
weight_hh_l[k]: the learnable hidden-hidden weights of the k-th layer,
|
|
of shape `(hidden_size, hidden_size)`
|
|
bias_ih_l[k]: the learnable input-hidden bias of the k-th layer,
|
|
of shape `(hidden_size)`
|
|
bias_hh_l[k]: the learnable hidden-hidden bias of the k-th layer,
|
|
of shape `(hidden_size)`
|
|
|
|
.. note::
|
|
All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
|
|
where :math:`k = \frac{1}{\text{hidden\_size}}`
|
|
|
|
.. note::
|
|
For bidirectional RNNs, forward and backward are directions 0 and 1 respectively.
|
|
Example of splitting the output layers when ``batch_first=False``:
|
|
``output.view(seq_len, batch, num_directions, hidden_size)``.
|
|
|
|
.. note::
|
|
``batch_first`` argument is ignored for unbatched inputs.
|
|
|
|
.. include:: ../cudnn_rnn_determinism.rst
|
|
|
|
.. include:: ../cudnn_persistent_rnn.rst
|
|
|
|
Examples::
|
|
|
|
>>> rnn = nn.RNN(10, 20, 2)
|
|
>>> input = torch.randn(5, 3, 10)
|
|
>>> h0 = torch.randn(2, 3, 20)
|
|
>>> output, hn = rnn(input, h0)
|
|
"""
|
|
|
|
@overload
|
|
def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1,
|
|
nonlinearity: str = 'tanh', bias: bool = True, batch_first: bool = False,
|
|
dropout: float = 0., bidirectional: bool = False, device=None,
|
|
dtype=None) -> None:
|
|
...
|
|
|
|
@overload
|
|
def __init__(self, *args, **kwargs):
|
|
...
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
if 'proj_size' in kwargs:
|
|
raise ValueError("proj_size argument is only supported for LSTM, not RNN or GRU")
|
|
if len(args) > 3:
|
|
self.nonlinearity = args[3]
|
|
args = args[:3] + args[4:]
|
|
else:
|
|
self.nonlinearity = kwargs.pop('nonlinearity', 'tanh')
|
|
if self.nonlinearity == 'tanh':
|
|
mode = 'RNN_TANH'
|
|
elif self.nonlinearity == 'relu':
|
|
mode = 'RNN_RELU'
|
|
else:
|
|
raise ValueError(f"Unknown nonlinearity '{self.nonlinearity}'. Select from 'tanh' or 'relu'.")
|
|
super().__init__(mode, *args, **kwargs)
|
|
|
|
@overload
|
|
@torch._jit_internal._overload_method # noqa: F811
|
|
def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
|
|
pass
|
|
|
|
@overload
|
|
@torch._jit_internal._overload_method # noqa: F811
|
|
def forward(self, input: PackedSequence, hx: Optional[Tensor] = None) -> Tuple[PackedSequence, Tensor]:
|
|
pass
|
|
|
|
def forward(self, input, hx=None): # noqa: F811
|
|
self._update_flat_weights()
|
|
|
|
num_directions = 2 if self.bidirectional else 1
|
|
orig_input = input
|
|
|
|
if isinstance(orig_input, PackedSequence):
|
|
input, batch_sizes, sorted_indices, unsorted_indices = input
|
|
max_batch_size = batch_sizes[0]
|
|
# script() is unhappy when max_batch_size is different type in cond branches, so we duplicate
|
|
if hx is None:
|
|
hx = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, self.hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
else:
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
else:
|
|
batch_sizes = None
|
|
if input.dim() not in (2, 3):
|
|
raise ValueError(f"RNN: Expected input to be 2D or 3D, got {input.dim()}D tensor instead")
|
|
is_batched = input.dim() == 3
|
|
batch_dim = 0 if self.batch_first else 1
|
|
if not is_batched:
|
|
input = input.unsqueeze(batch_dim)
|
|
if hx is not None:
|
|
if hx.dim() != 2:
|
|
raise RuntimeError(
|
|
f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor")
|
|
hx = hx.unsqueeze(1)
|
|
else:
|
|
if hx is not None and hx.dim() != 3:
|
|
raise RuntimeError(
|
|
f"For batched 3-D input, hx should also be 3-D but got {hx.dim()}-D tensor")
|
|
max_batch_size = input.size(0) if self.batch_first else input.size(1)
|
|
sorted_indices = None
|
|
unsorted_indices = None
|
|
if hx is None:
|
|
hx = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, self.hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
else:
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
|
|
assert hx is not None
|
|
self.check_forward_args(input, hx, batch_sizes)
|
|
assert self.mode == 'RNN_TANH' or self.mode == 'RNN_RELU'
|
|
if batch_sizes is None:
|
|
if self.mode == 'RNN_TANH':
|
|
result = _VF.rnn_tanh(input, hx, self._flat_weights, self.bias, self.num_layers,
|
|
self.dropout, self.training, self.bidirectional,
|
|
self.batch_first)
|
|
else:
|
|
result = _VF.rnn_relu(input, hx, self._flat_weights, self.bias, self.num_layers,
|
|
self.dropout, self.training, self.bidirectional,
|
|
self.batch_first)
|
|
else:
|
|
if self.mode == 'RNN_TANH':
|
|
result = _VF.rnn_tanh(input, batch_sizes, hx, self._flat_weights, self.bias,
|
|
self.num_layers, self.dropout, self.training,
|
|
self.bidirectional)
|
|
else:
|
|
result = _VF.rnn_relu(input, batch_sizes, hx, self._flat_weights, self.bias,
|
|
self.num_layers, self.dropout, self.training,
|
|
self.bidirectional)
|
|
|
|
output = result[0]
|
|
hidden = result[1]
|
|
|
|
if isinstance(orig_input, PackedSequence):
|
|
output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
|
|
return output_packed, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
if not is_batched: # type: ignore[possibly-undefined]
|
|
output = output.squeeze(batch_dim) # type: ignore[possibly-undefined]
|
|
hidden = hidden.squeeze(1)
|
|
|
|
return output, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
# XXX: LSTM and GRU implementation is different from RNNBase, this is because:
|
|
# 1. we want to support nn.LSTM and nn.GRU in TorchScript and TorchScript in
|
|
# its current state could not support the python Union Type or Any Type
|
|
# 2. TorchScript static typing does not allow a Function or Callable type in
|
|
# Dict values, so we have to separately call _VF instead of using _rnn_impls
|
|
# 3. This is temporary only and in the transition state that we want to make it
|
|
# on time for the release
|
|
#
|
|
# More discussion details in https://github.com/pytorch/pytorch/pull/23266
|
|
#
|
|
# TODO: remove the overriding implementations for LSTM and GRU when TorchScript
|
|
# support expressing these two modules generally.
|
|
|
|
|
|
class LSTM(RNNBase):
|
|
r"""__init__(input_size,hidden_size,num_layers=1,bias=True,batch_first=False,dropout=0.0,bidirectional=False,proj_size=0,device=None,dtype=None)
|
|
|
|
Apply a multi-layer long short-term memory (LSTM) RNN to an input sequence.
|
|
For each element in the input sequence, each layer computes the following
|
|
function:
|
|
|
|
.. math::
|
|
\begin{array}{ll} \\
|
|
i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{t-1} + b_{hi}) \\
|
|
f_t = \sigma(W_{if} x_t + b_{if} + W_{hf} h_{t-1} + b_{hf}) \\
|
|
g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{t-1} + b_{hg}) \\
|
|
o_t = \sigma(W_{io} x_t + b_{io} + W_{ho} h_{t-1} + b_{ho}) \\
|
|
c_t = f_t \odot c_{t-1} + i_t \odot g_t \\
|
|
h_t = o_t \odot \tanh(c_t) \\
|
|
\end{array}
|
|
|
|
where :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the cell
|
|
state at time `t`, :math:`x_t` is the input at time `t`, :math:`h_{t-1}`
|
|
is the hidden state of the layer at time `t-1` or the initial hidden
|
|
state at time `0`, and :math:`i_t`, :math:`f_t`, :math:`g_t`,
|
|
:math:`o_t` are the input, forget, cell, and output gates, respectively.
|
|
:math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.
|
|
|
|
In a multilayer LSTM, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
|
|
(:math:`l \ge 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
|
|
dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
|
|
variable which is :math:`0` with probability :attr:`dropout`.
|
|
|
|
If ``proj_size > 0`` is specified, LSTM with projections will be used. This changes
|
|
the LSTM cell in the following way. First, the dimension of :math:`h_t` will be changed from
|
|
``hidden_size`` to ``proj_size`` (dimensions of :math:`W_{hi}` will be changed accordingly).
|
|
Second, the output hidden state of each layer will be multiplied by a learnable projection
|
|
matrix: :math:`h_t = W_{hr}h_t`. Note that as a consequence of this, the output
|
|
of LSTM network will be of different shape as well. See Inputs/Outputs sections below for exact
|
|
dimensions of all variables. You can find more details in https://arxiv.org/abs/1402.1128.
|
|
|
|
Args:
|
|
input_size: The number of expected features in the input `x`
|
|
hidden_size: The number of features in the hidden state `h`
|
|
num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
|
|
would mean stacking two LSTMs together to form a `stacked LSTM`,
|
|
with the second LSTM taking in outputs of the first LSTM and
|
|
computing the final results. Default: 1
|
|
bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
|
|
Default: ``True``
|
|
batch_first: If ``True``, then the input and output tensors are provided
|
|
as `(batch, seq, feature)` instead of `(seq, batch, feature)`.
|
|
Note that this does not apply to hidden or cell states. See the
|
|
Inputs/Outputs sections below for details. Default: ``False``
|
|
dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
|
|
LSTM layer except the last layer, with dropout probability equal to
|
|
:attr:`dropout`. Default: 0
|
|
bidirectional: If ``True``, becomes a bidirectional LSTM. Default: ``False``
|
|
proj_size: If ``> 0``, will use LSTM with projections of corresponding size. Default: 0
|
|
|
|
Inputs: input, (h_0, c_0)
|
|
* **input**: tensor of shape :math:`(L, H_{in})` for unbatched input,
|
|
:math:`(L, N, H_{in})` when ``batch_first=False`` or
|
|
:math:`(N, L, H_{in})` when ``batch_first=True`` containing the features of
|
|
the input sequence. The input can also be a packed variable length sequence.
|
|
See :func:`torch.nn.utils.rnn.pack_padded_sequence` or
|
|
:func:`torch.nn.utils.rnn.pack_sequence` for details.
|
|
* **h_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
|
|
:math:`(D * \text{num\_layers}, N, H_{out})` containing the
|
|
initial hidden state for each element in the input sequence.
|
|
Defaults to zeros if (h_0, c_0) is not provided.
|
|
* **c_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{cell})` for unbatched input or
|
|
:math:`(D * \text{num\_layers}, N, H_{cell})` containing the
|
|
initial cell state for each element in the input sequence.
|
|
Defaults to zeros if (h_0, c_0) is not provided.
|
|
|
|
where:
|
|
|
|
.. math::
|
|
\begin{aligned}
|
|
N ={} & \text{batch size} \\
|
|
L ={} & \text{sequence length} \\
|
|
D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\
|
|
H_{in} ={} & \text{input\_size} \\
|
|
H_{cell} ={} & \text{hidden\_size} \\
|
|
H_{out} ={} & \text{proj\_size if } \text{proj\_size}>0 \text{ otherwise hidden\_size} \\
|
|
\end{aligned}
|
|
|
|
Outputs: output, (h_n, c_n)
|
|
* **output**: tensor of shape :math:`(L, D * H_{out})` for unbatched input,
|
|
:math:`(L, N, D * H_{out})` when ``batch_first=False`` or
|
|
:math:`(N, L, D * H_{out})` when ``batch_first=True`` containing the output features
|
|
`(h_t)` from the last layer of the LSTM, for each `t`. If a
|
|
:class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output
|
|
will also be a packed sequence. When ``bidirectional=True``, `output` will contain
|
|
a concatenation of the forward and reverse hidden states at each time step in the sequence.
|
|
* **h_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
|
|
:math:`(D * \text{num\_layers}, N, H_{out})` containing the
|
|
final hidden state for each element in the sequence. When ``bidirectional=True``,
|
|
`h_n` will contain a concatenation of the final forward and reverse hidden states, respectively.
|
|
* **c_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{cell})` for unbatched input or
|
|
:math:`(D * \text{num\_layers}, N, H_{cell})` containing the
|
|
final cell state for each element in the sequence. When ``bidirectional=True``,
|
|
`c_n` will contain a concatenation of the final forward and reverse cell states, respectively.
|
|
|
|
Attributes:
|
|
weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
|
|
`(W_ii|W_if|W_ig|W_io)`, of shape `(4*hidden_size, input_size)` for `k = 0`.
|
|
Otherwise, the shape is `(4*hidden_size, num_directions * hidden_size)`. If
|
|
``proj_size > 0`` was specified, the shape will be
|
|
`(4*hidden_size, num_directions * proj_size)` for `k > 0`
|
|
weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
|
|
`(W_hi|W_hf|W_hg|W_ho)`, of shape `(4*hidden_size, hidden_size)`. If ``proj_size > 0``
|
|
was specified, the shape will be `(4*hidden_size, proj_size)`.
|
|
bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
|
|
`(b_ii|b_if|b_ig|b_io)`, of shape `(4*hidden_size)`
|
|
bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
|
|
`(b_hi|b_hf|b_hg|b_ho)`, of shape `(4*hidden_size)`
|
|
weight_hr_l[k] : the learnable projection weights of the :math:`\text{k}^{th}` layer
|
|
of shape `(proj_size, hidden_size)`. Only present when ``proj_size > 0`` was
|
|
specified.
|
|
weight_ih_l[k]_reverse: Analogous to `weight_ih_l[k]` for the reverse direction.
|
|
Only present when ``bidirectional=True``.
|
|
weight_hh_l[k]_reverse: Analogous to `weight_hh_l[k]` for the reverse direction.
|
|
Only present when ``bidirectional=True``.
|
|
bias_ih_l[k]_reverse: Analogous to `bias_ih_l[k]` for the reverse direction.
|
|
Only present when ``bidirectional=True``.
|
|
bias_hh_l[k]_reverse: Analogous to `bias_hh_l[k]` for the reverse direction.
|
|
Only present when ``bidirectional=True``.
|
|
weight_hr_l[k]_reverse: Analogous to `weight_hr_l[k]` for the reverse direction.
|
|
Only present when ``bidirectional=True`` and ``proj_size > 0`` was specified.
|
|
|
|
.. note::
|
|
All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
|
|
where :math:`k = \frac{1}{\text{hidden\_size}}`
|
|
|
|
.. note::
|
|
For bidirectional LSTMs, forward and backward are directions 0 and 1 respectively.
|
|
Example of splitting the output layers when ``batch_first=False``:
|
|
``output.view(seq_len, batch, num_directions, hidden_size)``.
|
|
|
|
.. note::
|
|
For bidirectional LSTMs, `h_n` is not equivalent to the last element of `output`; the
|
|
former contains the final forward and reverse hidden states, while the latter contains the
|
|
final forward hidden state and the initial reverse hidden state.
|
|
|
|
.. note::
|
|
``batch_first`` argument is ignored for unbatched inputs.
|
|
|
|
.. note::
|
|
``proj_size`` should be smaller than ``hidden_size``.
|
|
|
|
.. include:: ../cudnn_rnn_determinism.rst
|
|
|
|
.. include:: ../cudnn_persistent_rnn.rst
|
|
|
|
Examples::
|
|
|
|
>>> rnn = nn.LSTM(10, 20, 2)
|
|
>>> input = torch.randn(5, 3, 10)
|
|
>>> h0 = torch.randn(2, 3, 20)
|
|
>>> c0 = torch.randn(2, 3, 20)
|
|
>>> output, (hn, cn) = rnn(input, (h0, c0))
|
|
"""
|
|
|
|
@overload
|
|
def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1, bias: bool = True,
|
|
batch_first: bool = False, dropout: float = 0., bidirectional: bool = False,
|
|
proj_size: int = 0, device=None, dtype=None) -> None:
|
|
...
|
|
|
|
@overload
|
|
def __init__(self, *args, **kwargs):
|
|
...
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__('LSTM', *args, **kwargs)
|
|
|
|
def get_expected_cell_size(self, input: Tensor, batch_sizes: Optional[Tensor]) -> Tuple[int, int, int]:
|
|
if batch_sizes is not None:
|
|
mini_batch = int(batch_sizes[0])
|
|
else:
|
|
mini_batch = input.size(0) if self.batch_first else input.size(1)
|
|
num_directions = 2 if self.bidirectional else 1
|
|
expected_hidden_size = (self.num_layers * num_directions,
|
|
mini_batch, self.hidden_size)
|
|
return expected_hidden_size
|
|
|
|
# In the future, we should prevent mypy from applying contravariance rules here.
|
|
# See torch/nn/modules/module.py::_forward_unimplemented
|
|
def check_forward_args(self, # type: ignore[override]
|
|
input: Tensor,
|
|
hidden: Tuple[Tensor, Tensor],
|
|
batch_sizes: Optional[Tensor],
|
|
):
|
|
self.check_input(input, batch_sizes)
|
|
self.check_hidden_size(hidden[0], self.get_expected_hidden_size(input, batch_sizes),
|
|
'Expected hidden[0] size {}, got {}')
|
|
self.check_hidden_size(hidden[1], self.get_expected_cell_size(input, batch_sizes),
|
|
'Expected hidden[1] size {}, got {}')
|
|
|
|
# Same as above, see torch/nn/modules/module.py::_forward_unimplemented
|
|
def permute_hidden(self, # type: ignore[override]
|
|
hx: Tuple[Tensor, Tensor],
|
|
permutation: Optional[Tensor]
|
|
) -> Tuple[Tensor, Tensor]:
|
|
if permutation is None:
|
|
return hx
|
|
return _apply_permutation(hx[0], permutation), _apply_permutation(hx[1], permutation)
|
|
|
|
# Same as above, see torch/nn/modules/module.py::_forward_unimplemented
|
|
@overload # type: ignore[override]
|
|
@torch._jit_internal._overload_method # noqa: F811
|
|
def forward(self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
|
|
) -> Tuple[Tensor, Tuple[Tensor, Tensor]]: # noqa: F811
|
|
pass
|
|
|
|
# Same as above, see torch/nn/modules/module.py::_forward_unimplemented
|
|
@overload
|
|
@torch._jit_internal._overload_method # noqa: F811
|
|
def forward(self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None
|
|
) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]: # noqa: F811
|
|
pass
|
|
|
|
def forward(self, input, hx=None): # noqa: F811
|
|
self._update_flat_weights()
|
|
|
|
orig_input = input
|
|
# xxx: isinstance check needs to be in conditional for TorchScript to compile
|
|
batch_sizes = None
|
|
do_permute = False
|
|
num_directions = 2 if self.bidirectional else 1
|
|
real_hidden_size = self.proj_size if self.proj_size > 0 else self.hidden_size
|
|
if isinstance(orig_input, PackedSequence):
|
|
input, batch_sizes, sorted_indices, unsorted_indices = input
|
|
max_batch_size = batch_sizes[0]
|
|
if hx is None:
|
|
h_zeros = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, real_hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
c_zeros = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, self.hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
hx = (h_zeros, c_zeros)
|
|
else:
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
else:
|
|
if input.dim() not in (2, 3):
|
|
raise ValueError(f"LSTM: Expected input to be 2D or 3D, got {input.dim()}D instead")
|
|
is_batched = input.dim() == 3
|
|
batch_dim = 0 if self.batch_first else 1
|
|
if not is_batched:
|
|
input = input.unsqueeze(batch_dim)
|
|
max_batch_size = input.size(0) if self.batch_first else input.size(1)
|
|
sorted_indices = None
|
|
unsorted_indices = None
|
|
if hx is None:
|
|
h_zeros = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, real_hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
c_zeros = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, self.hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
hx = (h_zeros, c_zeros)
|
|
self.check_forward_args(input, hx, batch_sizes)
|
|
else:
|
|
if is_batched:
|
|
if (hx[0].dim() != 3 or hx[1].dim() != 3):
|
|
msg = ("For batched 3-D input, hx and cx should "
|
|
f"also be 3-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors")
|
|
raise RuntimeError(msg)
|
|
else:
|
|
if hx[0].dim() != 2 or hx[1].dim() != 2:
|
|
msg = ("For unbatched 2-D input, hx and cx should "
|
|
f"also be 2-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors")
|
|
raise RuntimeError(msg)
|
|
hx = (hx[0].unsqueeze(1), hx[1].unsqueeze(1))
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
self.check_forward_args(input, hx, batch_sizes)
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
|
|
if batch_sizes is None:
|
|
result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,
|
|
self.dropout, self.training, self.bidirectional, self.batch_first)
|
|
else:
|
|
result = _VF.lstm(input, batch_sizes, hx, self._flat_weights, self.bias,
|
|
self.num_layers, self.dropout, self.training, self.bidirectional)
|
|
output = result[0]
|
|
hidden = result[1:]
|
|
# xxx: isinstance check needs to be in conditional for TorchScript to compile
|
|
if isinstance(orig_input, PackedSequence):
|
|
output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
|
|
return output_packed, self.permute_hidden(hidden, unsorted_indices)
|
|
else:
|
|
if not is_batched: # type: ignore[possibly-undefined]
|
|
output = output.squeeze(batch_dim) # type: ignore[possibly-undefined]
|
|
hidden = (hidden[0].squeeze(1), hidden[1].squeeze(1))
|
|
return output, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
|
|
class GRU(RNNBase):
|
|
r"""__init__(input_size,hidden_size,num_layers=1,bias=True,batch_first=False,dropout=0.0,bidirectional=False,device=None,dtype=None)
|
|
|
|
Apply a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
|
|
For each element in the input sequence, each layer computes the following
|
|
function:
|
|
|
|
.. math::
|
|
\begin{array}{ll}
|
|
r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
|
|
z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
|
|
n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\
|
|
h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)}
|
|
\end{array}
|
|
|
|
where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the input
|
|
at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer
|
|
at time `t-1` or the initial hidden state at time `0`, and :math:`r_t`,
|
|
:math:`z_t`, :math:`n_t` are the reset, update, and new gates, respectively.
|
|
:math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.
|
|
|
|
In a multilayer GRU, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
|
|
(:math:`l \ge 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
|
|
dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
|
|
variable which is :math:`0` with probability :attr:`dropout`.
|
|
|
|
Args:
|
|
input_size: The number of expected features in the input `x`
|
|
hidden_size: The number of features in the hidden state `h`
|
|
num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
|
|
would mean stacking two GRUs together to form a `stacked GRU`,
|
|
with the second GRU taking in outputs of the first GRU and
|
|
computing the final results. Default: 1
|
|
bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
|
|
Default: ``True``
|
|
batch_first: If ``True``, then the input and output tensors are provided
|
|
as `(batch, seq, feature)` instead of `(seq, batch, feature)`.
|
|
Note that this does not apply to hidden or cell states. See the
|
|
Inputs/Outputs sections below for details. Default: ``False``
|
|
dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
|
|
GRU layer except the last layer, with dropout probability equal to
|
|
:attr:`dropout`. Default: 0
|
|
bidirectional: If ``True``, becomes a bidirectional GRU. Default: ``False``
|
|
|
|
Inputs: input, h_0
|
|
* **input**: tensor of shape :math:`(L, H_{in})` for unbatched input,
|
|
:math:`(L, N, H_{in})` when ``batch_first=False`` or
|
|
:math:`(N, L, H_{in})` when ``batch_first=True`` containing the features of
|
|
the input sequence. The input can also be a packed variable length sequence.
|
|
See :func:`torch.nn.utils.rnn.pack_padded_sequence` or
|
|
:func:`torch.nn.utils.rnn.pack_sequence` for details.
|
|
* **h_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` or
|
|
:math:`(D * \text{num\_layers}, N, H_{out})`
|
|
containing the initial hidden state for the input sequence. Defaults to zeros if not provided.
|
|
|
|
where:
|
|
|
|
.. math::
|
|
\begin{aligned}
|
|
N ={} & \text{batch size} \\
|
|
L ={} & \text{sequence length} \\
|
|
D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\
|
|
H_{in} ={} & \text{input\_size} \\
|
|
H_{out} ={} & \text{hidden\_size}
|
|
\end{aligned}
|
|
|
|
Outputs: output, h_n
|
|
* **output**: tensor of shape :math:`(L, D * H_{out})` for unbatched input,
|
|
:math:`(L, N, D * H_{out})` when ``batch_first=False`` or
|
|
:math:`(N, L, D * H_{out})` when ``batch_first=True`` containing the output features
|
|
`(h_t)` from the last layer of the GRU, for each `t`. If a
|
|
:class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output
|
|
will also be a packed sequence.
|
|
* **h_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` or
|
|
:math:`(D * \text{num\_layers}, N, H_{out})` containing the final hidden state
|
|
for the input sequence.
|
|
|
|
Attributes:
|
|
weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
|
|
(W_ir|W_iz|W_in), of shape `(3*hidden_size, input_size)` for `k = 0`.
|
|
Otherwise, the shape is `(3*hidden_size, num_directions * hidden_size)`
|
|
weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
|
|
(W_hr|W_hz|W_hn), of shape `(3*hidden_size, hidden_size)`
|
|
bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
|
|
(b_ir|b_iz|b_in), of shape `(3*hidden_size)`
|
|
bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
|
|
(b_hr|b_hz|b_hn), of shape `(3*hidden_size)`
|
|
|
|
.. note::
|
|
All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
|
|
where :math:`k = \frac{1}{\text{hidden\_size}}`
|
|
|
|
.. note::
|
|
For bidirectional GRUs, forward and backward are directions 0 and 1 respectively.
|
|
Example of splitting the output layers when ``batch_first=False``:
|
|
``output.view(seq_len, batch, num_directions, hidden_size)``.
|
|
|
|
.. note::
|
|
``batch_first`` argument is ignored for unbatched inputs.
|
|
|
|
.. note::
|
|
The calculation of new gate :math:`n_t` subtly differs from the original paper and other frameworks.
|
|
In the original implementation, the Hadamard product :math:`(\odot)` between :math:`r_t` and the
|
|
previous hidden state :math:`h_{(t-1)}` is done before the multiplication with the weight matrix
|
|
`W` and addition of bias:
|
|
|
|
.. math::
|
|
\begin{aligned}
|
|
n_t = \tanh(W_{in} x_t + b_{in} + W_{hn} ( r_t \odot h_{(t-1)} ) + b_{hn})
|
|
\end{aligned}
|
|
|
|
This is in contrast to PyTorch implementation, which is done after :math:`W_{hn} h_{(t-1)}`
|
|
|
|
.. math::
|
|
\begin{aligned}
|
|
n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn}))
|
|
\end{aligned}
|
|
|
|
This implementation differs on purpose for efficiency.
|
|
|
|
.. include:: ../cudnn_persistent_rnn.rst
|
|
|
|
Examples::
|
|
|
|
>>> rnn = nn.GRU(10, 20, 2)
|
|
>>> input = torch.randn(5, 3, 10)
|
|
>>> h0 = torch.randn(2, 3, 20)
|
|
>>> output, hn = rnn(input, h0)
|
|
"""
|
|
|
|
@overload
|
|
def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1, bias: bool = True,
|
|
batch_first: bool = False, dropout: float = 0., bidirectional: bool = False,
|
|
device=None, dtype=None) -> None:
|
|
...
|
|
|
|
@overload
|
|
def __init__(self, *args, **kwargs):
|
|
...
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
if 'proj_size' in kwargs:
|
|
raise ValueError("proj_size argument is only supported for LSTM, not RNN or GRU")
|
|
super().__init__('GRU', *args, **kwargs)
|
|
|
|
@overload # type: ignore[override]
|
|
@torch._jit_internal._overload_method # noqa: F811
|
|
def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: # noqa: F811
|
|
pass
|
|
|
|
@overload
|
|
@torch._jit_internal._overload_method # noqa: F811
|
|
def forward(self, input: PackedSequence, hx: Optional[Tensor] = None) -> Tuple[PackedSequence, Tensor]: # noqa: F811
|
|
pass
|
|
|
|
def forward(self, input, hx=None): # noqa: F811
|
|
self._update_flat_weights()
|
|
|
|
orig_input = input
|
|
# xxx: isinstance check needs to be in conditional for TorchScript to compile
|
|
if isinstance(orig_input, PackedSequence):
|
|
input, batch_sizes, sorted_indices, unsorted_indices = input
|
|
max_batch_size = batch_sizes[0]
|
|
if hx is None:
|
|
num_directions = 2 if self.bidirectional else 1
|
|
hx = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, self.hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
else:
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
else:
|
|
batch_sizes = None
|
|
if input.dim() not in (2, 3):
|
|
raise ValueError(f"GRU: Expected input to be 2D or 3D, got {input.dim()}D instead")
|
|
is_batched = input.dim() == 3
|
|
batch_dim = 0 if self.batch_first else 1
|
|
if not is_batched:
|
|
input = input.unsqueeze(batch_dim)
|
|
if hx is not None:
|
|
if hx.dim() != 2:
|
|
raise RuntimeError(
|
|
f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor")
|
|
hx = hx.unsqueeze(1)
|
|
else:
|
|
if hx is not None and hx.dim() != 3:
|
|
raise RuntimeError(
|
|
f"For batched 3-D input, hx should also be 3-D but got {hx.dim()}-D tensor")
|
|
max_batch_size = input.size(0) if self.batch_first else input.size(1)
|
|
sorted_indices = None
|
|
unsorted_indices = None
|
|
if hx is None:
|
|
num_directions = 2 if self.bidirectional else 1
|
|
hx = torch.zeros(self.num_layers * num_directions,
|
|
max_batch_size, self.hidden_size,
|
|
dtype=input.dtype, device=input.device)
|
|
else:
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
|
|
self.check_forward_args(input, hx, batch_sizes)
|
|
if batch_sizes is None:
|
|
result = _VF.gru(input, hx, self._flat_weights, self.bias, self.num_layers,
|
|
self.dropout, self.training, self.bidirectional, self.batch_first)
|
|
else:
|
|
result = _VF.gru(input, batch_sizes, hx, self._flat_weights, self.bias,
|
|
self.num_layers, self.dropout, self.training, self.bidirectional)
|
|
output = result[0]
|
|
hidden = result[1]
|
|
|
|
# xxx: isinstance check needs to be in conditional for TorchScript to compile
|
|
if isinstance(orig_input, PackedSequence):
|
|
output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
|
|
return output_packed, self.permute_hidden(hidden, unsorted_indices)
|
|
else:
|
|
if not is_batched: # type: ignore[possibly-undefined]
|
|
output = output.squeeze(batch_dim) # type: ignore[possibly-undefined]
|
|
hidden = hidden.squeeze(1)
|
|
|
|
return output, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
|
|
class RNNCellBase(Module):
|
|
__constants__ = ['input_size', 'hidden_size', 'bias']
|
|
|
|
input_size: int
|
|
hidden_size: int
|
|
bias: bool
|
|
weight_ih: Tensor
|
|
weight_hh: Tensor
|
|
# WARNING: bias_ih and bias_hh purposely not defined here.
|
|
# See https://github.com/pytorch/pytorch/issues/39670
|
|
|
|
def __init__(self, input_size: int, hidden_size: int, bias: bool, num_chunks: int,
|
|
device=None, dtype=None) -> None:
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__()
|
|
self.input_size = input_size
|
|
self.hidden_size = hidden_size
|
|
self.bias = bias
|
|
self.weight_ih = Parameter(torch.empty((num_chunks * hidden_size, input_size), **factory_kwargs))
|
|
self.weight_hh = Parameter(torch.empty((num_chunks * hidden_size, hidden_size), **factory_kwargs))
|
|
if bias:
|
|
self.bias_ih = Parameter(torch.empty(num_chunks * hidden_size, **factory_kwargs))
|
|
self.bias_hh = Parameter(torch.empty(num_chunks * hidden_size, **factory_kwargs))
|
|
else:
|
|
self.register_parameter('bias_ih', None)
|
|
self.register_parameter('bias_hh', None)
|
|
|
|
self.reset_parameters()
|
|
|
|
def extra_repr(self) -> str:
|
|
s = '{input_size}, {hidden_size}'
|
|
if 'bias' in self.__dict__ and self.bias is not True:
|
|
s += ', bias={bias}'
|
|
if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh":
|
|
s += ', nonlinearity={nonlinearity}'
|
|
return s.format(**self.__dict__)
|
|
|
|
def reset_parameters(self) -> None:
|
|
stdv = 1.0 / math.sqrt(self.hidden_size) if self.hidden_size > 0 else 0
|
|
for weight in self.parameters():
|
|
init.uniform_(weight, -stdv, stdv)
|
|
|
|
|
|
class RNNCell(RNNCellBase):
|
|
r"""An Elman RNN cell with tanh or ReLU non-linearity.
|
|
|
|
.. math::
|
|
|
|
h' = \tanh(W_{ih} x + b_{ih} + W_{hh} h + b_{hh})
|
|
|
|
If :attr:`nonlinearity` is `'relu'`, then ReLU is used in place of tanh.
|
|
|
|
Args:
|
|
input_size: The number of expected features in the input `x`
|
|
hidden_size: The number of features in the hidden state `h`
|
|
bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
|
|
Default: ``True``
|
|
nonlinearity: The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``
|
|
|
|
Inputs: input, hidden
|
|
- **input**: tensor containing input features
|
|
- **hidden**: tensor containing the initial hidden state
|
|
Defaults to zero if not provided.
|
|
|
|
Outputs: h'
|
|
- **h'** of shape `(batch, hidden_size)`: tensor containing the next hidden state
|
|
for each element in the batch
|
|
|
|
Shape:
|
|
- input: :math:`(N, H_{in})` or :math:`(H_{in})` tensor containing input features where
|
|
:math:`H_{in}` = `input_size`.
|
|
- hidden: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the initial hidden
|
|
state where :math:`H_{out}` = `hidden_size`. Defaults to zero if not provided.
|
|
- output: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the next hidden state.
|
|
|
|
Attributes:
|
|
weight_ih: the learnable input-hidden weights, of shape
|
|
`(hidden_size, input_size)`
|
|
weight_hh: the learnable hidden-hidden weights, of shape
|
|
`(hidden_size, hidden_size)`
|
|
bias_ih: the learnable input-hidden bias, of shape `(hidden_size)`
|
|
bias_hh: the learnable hidden-hidden bias, of shape `(hidden_size)`
|
|
|
|
.. note::
|
|
All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
|
|
where :math:`k = \frac{1}{\text{hidden\_size}}`
|
|
|
|
Examples::
|
|
|
|
>>> rnn = nn.RNNCell(10, 20)
|
|
>>> input = torch.randn(6, 3, 10)
|
|
>>> hx = torch.randn(3, 20)
|
|
>>> output = []
|
|
>>> for i in range(6):
|
|
... hx = rnn(input[i], hx)
|
|
... output.append(hx)
|
|
"""
|
|
|
|
__constants__ = ['input_size', 'hidden_size', 'bias', 'nonlinearity']
|
|
nonlinearity: str
|
|
|
|
def __init__(self, input_size: int, hidden_size: int, bias: bool = True, nonlinearity: str = "tanh",
|
|
device=None, dtype=None) -> None:
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__(input_size, hidden_size, bias, num_chunks=1, **factory_kwargs)
|
|
self.nonlinearity = nonlinearity
|
|
|
|
def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
|
|
if input.dim() not in (1, 2):
|
|
raise ValueError(f"RNNCell: Expected input to be 1D or 2D, got {input.dim()}D instead")
|
|
if hx is not None and hx.dim() not in (1, 2):
|
|
raise ValueError(f"RNNCell: Expected hidden to be 1D or 2D, got {hx.dim()}D instead")
|
|
is_batched = input.dim() == 2
|
|
if not is_batched:
|
|
input = input.unsqueeze(0)
|
|
|
|
if hx is None:
|
|
hx = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
|
|
else:
|
|
hx = hx.unsqueeze(0) if not is_batched else hx
|
|
|
|
if self.nonlinearity == "tanh":
|
|
ret = _VF.rnn_tanh_cell(
|
|
input, hx,
|
|
self.weight_ih, self.weight_hh,
|
|
self.bias_ih, self.bias_hh,
|
|
)
|
|
elif self.nonlinearity == "relu":
|
|
ret = _VF.rnn_relu_cell(
|
|
input, hx,
|
|
self.weight_ih, self.weight_hh,
|
|
self.bias_ih, self.bias_hh,
|
|
)
|
|
else:
|
|
ret = input # TODO: remove when jit supports exception flow
|
|
raise RuntimeError(
|
|
f"Unknown nonlinearity: {self.nonlinearity}")
|
|
|
|
if not is_batched:
|
|
ret = ret.squeeze(0)
|
|
|
|
return ret
|
|
|
|
|
|
class LSTMCell(RNNCellBase):
|
|
r"""A long short-term memory (LSTM) cell.
|
|
|
|
.. math::
|
|
|
|
\begin{array}{ll}
|
|
i = \sigma(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\
|
|
f = \sigma(W_{if} x + b_{if} + W_{hf} h + b_{hf}) \\
|
|
g = \tanh(W_{ig} x + b_{ig} + W_{hg} h + b_{hg}) \\
|
|
o = \sigma(W_{io} x + b_{io} + W_{ho} h + b_{ho}) \\
|
|
c' = f \odot c + i \odot g \\
|
|
h' = o \odot \tanh(c') \\
|
|
\end{array}
|
|
|
|
where :math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.
|
|
|
|
Args:
|
|
input_size: The number of expected features in the input `x`
|
|
hidden_size: The number of features in the hidden state `h`
|
|
bias: If ``False``, then the layer does not use bias weights `b_ih` and
|
|
`b_hh`. Default: ``True``
|
|
|
|
Inputs: input, (h_0, c_0)
|
|
- **input** of shape `(batch, input_size)` or `(input_size)`: tensor containing input features
|
|
- **h_0** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the initial hidden state
|
|
- **c_0** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the initial cell state
|
|
|
|
If `(h_0, c_0)` is not provided, both **h_0** and **c_0** default to zero.
|
|
|
|
Outputs: (h_1, c_1)
|
|
- **h_1** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the next hidden state
|
|
- **c_1** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the next cell state
|
|
|
|
Attributes:
|
|
weight_ih: the learnable input-hidden weights, of shape
|
|
`(4*hidden_size, input_size)`
|
|
weight_hh: the learnable hidden-hidden weights, of shape
|
|
`(4*hidden_size, hidden_size)`
|
|
bias_ih: the learnable input-hidden bias, of shape `(4*hidden_size)`
|
|
bias_hh: the learnable hidden-hidden bias, of shape `(4*hidden_size)`
|
|
|
|
.. note::
|
|
All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
|
|
where :math:`k = \frac{1}{\text{hidden\_size}}`
|
|
|
|
On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.
|
|
|
|
Examples::
|
|
|
|
>>> rnn = nn.LSTMCell(10, 20) # (input_size, hidden_size)
|
|
>>> input = torch.randn(2, 3, 10) # (time_steps, batch, input_size)
|
|
>>> hx = torch.randn(3, 20) # (batch, hidden_size)
|
|
>>> cx = torch.randn(3, 20)
|
|
>>> output = []
|
|
>>> for i in range(input.size()[0]):
|
|
... hx, cx = rnn(input[i], (hx, cx))
|
|
... output.append(hx)
|
|
>>> output = torch.stack(output, dim=0)
|
|
"""
|
|
|
|
def __init__(self, input_size: int, hidden_size: int, bias: bool = True,
|
|
device=None, dtype=None) -> None:
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__(input_size, hidden_size, bias, num_chunks=4, **factory_kwargs)
|
|
|
|
def forward(self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]:
|
|
if input.dim() not in (1, 2):
|
|
raise ValueError(f"LSTMCell: Expected input to be 1D or 2D, got {input.dim()}D instead")
|
|
if hx is not None:
|
|
for idx, value in enumerate(hx):
|
|
if value.dim() not in (1, 2):
|
|
raise ValueError(f"LSTMCell: Expected hx[{idx}] to be 1D or 2D, got {value.dim()}D instead")
|
|
is_batched = input.dim() == 2
|
|
if not is_batched:
|
|
input = input.unsqueeze(0)
|
|
|
|
if hx is None:
|
|
zeros = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
|
|
hx = (zeros, zeros)
|
|
else:
|
|
hx = (hx[0].unsqueeze(0), hx[1].unsqueeze(0)) if not is_batched else hx
|
|
|
|
ret = _VF.lstm_cell(
|
|
input, hx,
|
|
self.weight_ih, self.weight_hh,
|
|
self.bias_ih, self.bias_hh,
|
|
)
|
|
|
|
if not is_batched:
|
|
ret = (ret[0].squeeze(0), ret[1].squeeze(0))
|
|
return ret
|
|
|
|
|
|
class GRUCell(RNNCellBase):
|
|
r"""A gated recurrent unit (GRU) cell.
|
|
|
|
.. math::
|
|
|
|
\begin{array}{ll}
|
|
r = \sigma(W_{ir} x + b_{ir} + W_{hr} h + b_{hr}) \\
|
|
z = \sigma(W_{iz} x + b_{iz} + W_{hz} h + b_{hz}) \\
|
|
n = \tanh(W_{in} x + b_{in} + r \odot (W_{hn} h + b_{hn})) \\
|
|
h' = (1 - z) \odot n + z \odot h
|
|
\end{array}
|
|
|
|
where :math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.
|
|
|
|
Args:
|
|
input_size: The number of expected features in the input `x`
|
|
hidden_size: The number of features in the hidden state `h`
|
|
bias: If ``False``, then the layer does not use bias weights `b_ih` and
|
|
`b_hh`. Default: ``True``
|
|
|
|
Inputs: input, hidden
|
|
- **input** : tensor containing input features
|
|
- **hidden** : tensor containing the initial hidden
|
|
state for each element in the batch.
|
|
Defaults to zero if not provided.
|
|
|
|
Outputs: h'
|
|
- **h'** : tensor containing the next hidden state
|
|
for each element in the batch
|
|
|
|
Shape:
|
|
- input: :math:`(N, H_{in})` or :math:`(H_{in})` tensor containing input features where
|
|
:math:`H_{in}` = `input_size`.
|
|
- hidden: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the initial hidden
|
|
state where :math:`H_{out}` = `hidden_size`. Defaults to zero if not provided.
|
|
- output: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the next hidden state.
|
|
|
|
Attributes:
|
|
weight_ih: the learnable input-hidden weights, of shape
|
|
`(3*hidden_size, input_size)`
|
|
weight_hh: the learnable hidden-hidden weights, of shape
|
|
`(3*hidden_size, hidden_size)`
|
|
bias_ih: the learnable input-hidden bias, of shape `(3*hidden_size)`
|
|
bias_hh: the learnable hidden-hidden bias, of shape `(3*hidden_size)`
|
|
|
|
.. note::
|
|
All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
|
|
where :math:`k = \frac{1}{\text{hidden\_size}}`
|
|
|
|
On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.
|
|
|
|
Examples::
|
|
|
|
>>> rnn = nn.GRUCell(10, 20)
|
|
>>> input = torch.randn(6, 3, 10)
|
|
>>> hx = torch.randn(3, 20)
|
|
>>> output = []
|
|
>>> for i in range(6):
|
|
... hx = rnn(input[i], hx)
|
|
... output.append(hx)
|
|
"""
|
|
|
|
def __init__(self, input_size: int, hidden_size: int, bias: bool = True,
|
|
device=None, dtype=None) -> None:
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__(input_size, hidden_size, bias, num_chunks=3, **factory_kwargs)
|
|
|
|
def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
|
|
if input.dim() not in (1, 2):
|
|
raise ValueError(f"GRUCell: Expected input to be 1D or 2D, got {input.dim()}D instead")
|
|
if hx is not None and hx.dim() not in (1, 2):
|
|
raise ValueError(f"GRUCell: Expected hidden to be 1D or 2D, got {hx.dim()}D instead")
|
|
is_batched = input.dim() == 2
|
|
if not is_batched:
|
|
input = input.unsqueeze(0)
|
|
|
|
if hx is None:
|
|
hx = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
|
|
else:
|
|
hx = hx.unsqueeze(0) if not is_batched else hx
|
|
|
|
ret = _VF.gru_cell(
|
|
input, hx,
|
|
self.weight_ih, self.weight_hh,
|
|
self.bias_ih, self.bias_hh,
|
|
)
|
|
|
|
if not is_batched:
|
|
ret = ret.squeeze(0)
|
|
|
|
return ret
|