forked from mindspore-Ecosystem/mindspore
!22765 add argument check for rnns and rnn cells and modify description of them
Merge pull request !22765 from 吕昱峰（Nate.River）/gru_op
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6ac2d8228f
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@ -36,6 +36,10 @@ def _init_state(shape, dtype, is_lstm):
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return (hx, cx)
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return hx
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@constexpr
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def _check_input_dtype(input_dtype, param_name, allow_dtypes, cls_name):
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validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)
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def _rnn_tanh_cell(inputs, hidden, w_ih, w_hh, b_ih, b_hh):
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'''RNN cell function with tanh activation'''
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if b_ih is None:
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@ -177,10 +181,14 @@ class _RNNBase(Cell):
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validator.check_positive_int(hidden_size, "hidden_size", self.cls_name)
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validator.check_positive_int(input_size, "input_size", self.cls_name)
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validator.check_positive_int(num_layers, "num_layers", self.cls_name)
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validator.check_is_float(dropout, "dropout", self.cls_name)
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validator.check_value_type("has_bias", has_bias, [bool], self.cls_name)
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validator.check_value_type("batch_first", batch_first, [bool], self.cls_name)
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if not 0 <= dropout <= 1:
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raise ValueError("dropout should be a number in range [0, 1] "
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validator.check_value_type("bidirectional", bidirectional, [bool], self.cls_name)
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if not 0 <= dropout < 1:
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raise ValueError(f"For '{self.cls_name}', "
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"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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@ -326,6 +334,14 @@ class _RNNBase(Cell):
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def construct(self, x, hx=None, seq_length=None):
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'''Defines the RNN like operators performed'''
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x_dtype = P.dtype(x)
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hx_dtype = P.dtype(hx)
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_check_input_dtype(x_dtype, "x", [mstype.float32], self.cls_name)
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_check_input_dtype(hx_dtype, "hx", [mstype.float32], self.cls_name)
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if seq_length is not None:
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seq_length_dtype = P.dtype(seq_length)
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_check_input_dtype(seq_length_dtype, "seq_length", [mstype.int32, mstype.int64], self.cls_name)
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max_batch_size = x.shape[0] if self.batch_first else x.shape[1]
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num_directions = 2 if self.bidirectional else 1
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if hx is None:
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@ -360,21 +376,22 @@ class RNN(_RNNBase):
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Args:
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input_size (int): Number of features of input.
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hidden_size (int): Number of features of hidden layer.
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num_layers (int): Number of layers of stacked GRU. Default: 1.
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num_layers (int): Number of layers of stacked RNN. Default: 1.
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nonlinearity (str): The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``
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has_bias (bool): Whether the cell has bias `b_ih` and `b_hh`. Default: True.
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batch_first (bool): Specifies whether the first dimension of input `x` is batch_size. Default: False.
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dropout (float, int): If not 0, append `Dropout` layer on the outputs of each
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LSTM layer except the last layer. Default 0. The range of dropout is [0.0, 1.0].
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bidirectional (bool): Specifies whether it is a bidirectional GRU. Default: False.
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RNN layer except the last layer. Default 0. The range of dropout is [0.0, 1.0).
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bidirectional (bool): Specifies whether it is a bidirectional RNN,
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num_directions=2 if bidirectional=True otherwise 1. Default: False.
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Inputs:
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- **x** (Tensor) - Tensor of shape (seq_len, batch_size, `input_size`) or
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(batch_size, seq_len, `input_size`).
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- **hx** (Tensor) - Tensor of data type mindspore.float32 or
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mindspore.float16 and shape (num_directions * `num_layers`, batch_size, `hidden_size`).
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Data type of `hx` must be the same as `x`.
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- **seq_length** (Tensor) - The length of each batch. Tensor of shape :math:`(\text{batch_size})`.
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- **x** (Tensor) - Tensor of data type mindspore.float32 and
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shape (seq_len, batch_size, `input_size`) or (batch_size, seq_len, `input_size`).
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- **hx** (Tensor) - Tensor of data type mindspore.float32 and
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shape (num_directions * `num_layers`, batch_size, `hidden_size`). Data type of `hx` must be the same as `x`.
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- **seq_length** (Tensor) - The length of each batch.
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Tensor of shape :math:`(\text{batch_size})`. Default: None.
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Outputs:
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Tuple, a tuple contains (`output`, `h_n`).
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@ -386,10 +403,11 @@ class RNN(_RNNBase):
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TypeError: If `input_size`, `hidden_size` or `num_layers` is not an int.
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TypeError: If `has_bias`, `batch_first` or `bidirectional` is not a bool.
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TypeError: If `dropout` is neither a float nor an int.
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ValueError: If `dropout` is not in range [0.0, 1.0].
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ValueError: If `dropout` is not in range [0.0, 1.0).
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ValueError: If `nonlinearity` is not in ['tanh', 'relu'].
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> net = nn.RNN(10, 16, 2, has_bias=True, batch_first=True, bidirectional=False)
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@ -452,16 +470,17 @@ class GRU(_RNNBase):
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has_bias (bool): Whether the cell has bias `b_ih` and `b_hh`. Default: True.
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batch_first (bool): Specifies whether the first dimension of input `x` is batch_size. Default: False.
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dropout (float, int): If not 0, append `Dropout` layer on the outputs of each
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LSTM layer except the last layer. Default 0. The range of dropout is [0.0, 1.0].
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bidirectional (bool): Specifies whether it is a bidirectional GRU. Default: False.
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GRU layer except the last layer. Default 0. The range of dropout is [0.0, 1.0).
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bidirectional (bool): Specifies whether it is a bidirectional GRU,
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num_directions=2 if bidirectional=True otherwise 1. Default: False.
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Inputs:
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- **x** (Tensor) - Tensor of shape (seq_len, batch_size, `input_size`) or
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(batch_size, seq_len, `input_size`).
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- **hx** (Tensor) - Tensor of data type mindspore.float32 or
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mindspore.float16 and shape (num_directions * `num_layers`, batch_size, `hidden_size`).
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Data type of `hx` must be the same as `x`.
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- **seq_length** (Tensor) - The length of each batch. Tensor of shape :math:`(\text{batch_size})`.
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- **x** (Tensor) - Tensor of data type mindspore.float32 and
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shape (seq_len, batch_size, `input_size`) or (batch_size, seq_len, `input_size`).
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- **hx** (Tensor) - Tensor of data type mindspore.float32 and
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shape (num_directions * `num_layers`, batch_size, `hidden_size`). Data type of `hx` must be the same as `x`.
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- **seq_length** (Tensor) - The length of each batch.
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Tensor of shape :math:`(\text{batch_size})`. Default: None.
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Outputs:
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Tuple, a tuple contains (`output`, `h_n`).
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@ -473,9 +492,10 @@ class GRU(_RNNBase):
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TypeError: If `input_size`, `hidden_size` or `num_layers` is not an int.
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TypeError: If `has_bias`, `batch_first` or `bidirectional` is not a bool.
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TypeError: If `dropout` is neither a float nor an int.
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ValueError: If `dropout` is not in range [0.0, 1.0].
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ValueError: If `dropout` is not in range [0.0, 1.0).
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> net = nn.GRU(10, 16, 2, has_bias=True, batch_first=True, bidirectional=False)
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@ -504,6 +524,9 @@ class _RNNCellBase(Cell):
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if has_bias:
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self.bias_ih = Parameter(Tensor(np.random.randn(num_chunks * hidden_size).astype(np.float32)))
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self.bias_hh = Parameter(Tensor(np.random.randn(num_chunks * hidden_size).astype(np.float32)))
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else:
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self.bias_ih = None
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self.bias_ih = None
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self.reset_parameters()
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def reset_parameters(self):
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@ -530,9 +553,8 @@ class RNNCell(_RNNCellBase):
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nonlinearity (str): The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``
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Inputs:
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- **input** (Tensor) - Tensor of shape (batch_size, `input_size`).
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- **hidden** (Tensor) - Tensor of data type mindspore.float32 or
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mindspore.float16 and shape (batch_size, `hidden_size`).
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- **x** (Tensor) - Tensor of shape (batch_size, `input_size`).
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- **hx** (Tensor) - Tensor of data type mindspore.float32 and shape (batch_size, `hidden_size`).
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Data type of `hx` must be the same as `x`.
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Outputs:
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@ -544,6 +566,7 @@ class RNNCell(_RNNCellBase):
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ValueError: If `nonlinearity` is not in ['tanh', 'relu'].
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> net = nn.RNNCell(10, 16)
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@ -551,9 +574,9 @@ class RNNCell(_RNNCellBase):
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>>> hx = Tensor(np.ones([3, 16]).astype(np.float32))
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>>> output = []
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>>> for i in range(5):
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>>> hx = net(input[i], hx)
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>>> hx = net(x[i], hx)
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>>> output.append(hx)
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>>> output[0]
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>>> print(output[0].shape)
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(3, 16)
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"""
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_non_linearity = ['tanh', 'relu']
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@ -596,9 +619,8 @@ class GRUCell(_RNNCellBase):
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has_bias (bool): Whether the cell has bias `b_ih` and `b_hh`. Default: True.
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Inputs:
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- **input** (Tensor) - Tensor of shape (batch_size, `input_size`).
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- **hidden** (Tensor) - Tensor of data type mindspore.float32 or
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mindspore.float16 and shape (batch_size, `hidden_size`).
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- **x** (Tensor) - Tensor of shape (batch_size, `input_size`).
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- **hx** (Tensor) - Tensor of data type mindspore.float32 and shape (batch_size, `hidden_size`).
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Data type of `hx` must be the same as `x`.
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Outputs:
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@ -609,6 +631,7 @@ class GRUCell(_RNNCellBase):
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TypeError: If `has_bias` is not a bool.
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> net = nn.GRUCell(10, 16)
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@ -616,9 +639,9 @@ class GRUCell(_RNNCellBase):
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>>> hx = Tensor(np.ones([3, 16]).astype(np.float32))
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>>> output = []
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>>> for i in range(5):
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>>> hx = net(input[i], hx)
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>>> hx = net(x[i], hx)
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>>> output.append(hx)
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>>> output[0]
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>>> print(output[0].shape)
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(3, 16)
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"""
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def __init__(self, input_size: int, hidden_size: int, has_bias: bool = True):
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