Source code for mindspore.nn.layer.lstm

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"""lstm"""
import math
import numpy as np
import mindspore.context as context
import mindspore.common.dtype as mstype
from mindspore.ops.primitive import constexpr
from mindspore._checkparam import Validator as validator
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.tensor import Tensor
from mindspore.nn.cell import Cell
from mindspore import nn
from mindspore.ops import operations as P
from mindspore.ops import functional as F


__all__ = ['LSTM', 'LSTMCell']


@constexpr
def _create_sequence_length(shape):
    num_step, batch_size, _ = shape
    sequence_length = Tensor(np.ones(batch_size, np.int32) * num_step, mstype.int32)
    return sequence_length


@constexpr
def _check_input_dtype(input_dtype, param_name, allow_dtypes, cls_name):
    validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)


@constexpr
def _check_input_3d(input_shape, param_name, func_name):
    if len(input_shape) != 3:
        raise ValueError(f"{func_name} {param_name} should be 3d, but got shape {input_shape}")


[docs]class LSTM(Cell): r""" Stacked LSTM (Long Short-Term Memory) layers. Apply LSTM layer to the input. There are two pipelines connecting two consecutive cells in a LSTM model; one is cell state pipeline and the other is hidden state pipeline. Denote two consecutive time nodes as :math:`t-1` and :math:`t`. Given an input :math:`x_t` at time :math:`t`, an hidden state :math:`h_{t-1}` and an cell state :math:`c_{t-1}` of the layer at time :math:`{t-1}`, the cell state and hidden state at time :math:`t` is computed using an gating mechanism. Input gate :math:`i_t` is designed to protect the cell from perturbation by irrelevant inputs. Forget gate :math:`f_t` affords protection of the cell by forgetting some information in the past, which is stored in :math:`h_{t-1}`. Output gate :math:`o_t` protects other units from perturbation by currently irrelevant memory contents. Candidate cell state :math:`\tilde{c}_t` is calculated with the current input, on which the input gate will be applied. Finally, current cell state :math:`c_{t}` and hidden state :math:`h_{t}` are computed with the calculated gates and cell states. The complete formulation is as follows. .. math:: \begin{array}{ll} \\ i_t = \sigma(W_{ix} x_t + b_{ix} + W_{ih} h_{(t-1)} + b_{ih}) \\ f_t = \sigma(W_{fx} x_t + b_{fx} + W_{fh} h_{(t-1)} + b_{fh}) \\ \tilde{c}_t = \tanh(W_{cx} x_t + b_{cx} + W_{ch} h_{(t-1)} + b_{ch}) \\ o_t = \sigma(W_{ox} x_t + b_{ox} + W_{oh} h_{(t-1)} + b_{oh}) \\ c_t = f_t * c_{(t-1)} + i_t * \tilde{c}_t \\ h_t = o_t * \tanh(c_t) \\ \end{array} Here :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product. :math:`W, b` are learnable weights between the output and the input in the formula. For instance, :math:`W_{ix}, b_{ix}` are the weight and bias used to transform from input :math:`x` to :math:`i`. Details can be found in paper `LONG SHORT-TERM MEMORY <https://www.bioinf.jku.at/publications/older/2604.pdf>`_ and `Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling <https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43905.pdf>`_. Args: input_size (int): Number of features of input. hidden_size (int): Number of features of hidden layer. num_layers (int): Number of layers of stacked LSTM . Default: 1. has_bias (bool): Whether the cell has bias `b_ih` and `b_hh`. Default: True. batch_first (bool): Specifies whether the first dimension of input is batch_size. Default: False. dropout (float, int): If not 0, append `Dropout` layer on the outputs of each LSTM layer except the last layer. Default 0. The range of dropout is [0.0, 1.0]. bidirectional (bool): Specifies whether it is a bidirectional LSTM. Default: False. Inputs: - **input** (Tensor) - Tensor of shape (seq_len, batch_size, `input_size`) or (batch_size, seq_len, `input_size`). - **hx** (tuple) - A tuple of two Tensors (h_0, c_0) both of data type mindspore.float32 or mindspore.float16 and shape (num_directions * `num_layers`, batch_size, `hidden_size`). Data type of `hx` must be the same as `input`. Outputs: Tuple, a tuple contains (`output`, (`h_n`, `c_n`)). - **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`). - **hx_n** (tuple) - A tuple of two Tensor (h_n, c_n) both of shape (num_directions * `num_layers`, batch_size, `hidden_size`). Raises: TypeError: If `input_size`, `hidden_size` or `num_layers` is not an int. TypeError: If `has_bias`, `batch_first` or `bidirectional` is not a bool. TypeError: If `dropout` is neither a float nor an int. ValueError: If `dropout` is not in range [0.0, 1.0]. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> net = nn.LSTM(10, 16, 2, has_bias=True, batch_first=True, bidirectional=False) >>> input = Tensor(np.ones([3, 5, 10]).astype(np.float32)) >>> h0 = Tensor(np.ones([1 * 2, 3, 16]).astype(np.float32)) >>> c0 = Tensor(np.ones([1 * 2, 3, 16]).astype(np.float32)) >>> output, (hn, cn) = net(input, (h0, c0)) >>> print(output.shape) (3, 5, 16) """ def __init__(self, input_size, hidden_size, num_layers=1, has_bias=True, batch_first=False, dropout=0, bidirectional=False): super(LSTM, self).__init__() validator.check_value_type("batch_first", batch_first, [bool], self.cls_name) validator.check_positive_int(hidden_size, "hidden_size", self.cls_name) validator.check_positive_int(num_layers, "num_layers", self.cls_name) self.is_ascend = context.get_context("device_target") == "Ascend" self.batch_first = batch_first self.transpose = P.Transpose() self.num_layers = num_layers self.bidirectional = bidirectional self.dropout = dropout self.lstm = P.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, has_bias=has_bias, bidirectional=bidirectional, dropout=float(dropout)) weight_size = 0 gate_size = 4 * hidden_size stdv = 1 / math.sqrt(hidden_size) num_directions = 2 if bidirectional else 1 if self.is_ascend: self.reverse_seq = P.ReverseSequence(batch_dim=1, seq_dim=0) self.concat = P.Concat(axis=0) self.concat_2dim = P.Concat(axis=2) self.cast = P.Cast() self.shape = P.Shape() if dropout < 0 or dropout > 1: raise ValueError("For LSTM, dropout must be a number in range [0, 1], but got {}".format(dropout)) if dropout == 1: self.dropout_op = P.ZerosLike() else: self.dropout_op = nn.Dropout(float(1 - dropout)) b0 = np.zeros(gate_size, dtype=np.float16) self.w_list = [] self.b_list = [] self.rnns_fw = P.DynamicRNN(forget_bias=0.0) self.rnns_bw = P.DynamicRNN(forget_bias=0.0) for layer in range(num_layers): w_shape = input_size if layer == 0 else (num_directions * hidden_size) w_np = np.random.uniform(-stdv, stdv, (w_shape + hidden_size, gate_size)).astype(np.float16) self.w_list.append(Parameter( initializer(Tensor(w_np), [w_shape + hidden_size, gate_size]), name='weight_fw' + str(layer))) if has_bias: b_np = np.random.uniform(-stdv, stdv, gate_size).astype(np.float16) self.b_list.append(Parameter(initializer(Tensor(b_np), [gate_size]), name='bias_fw' + str(layer))) else: self.b_list.append(Parameter(initializer(Tensor(b0), [gate_size]), name='bias_fw' + str(layer))) if bidirectional: w_bw_np = np.random.uniform(-stdv, stdv, (w_shape + hidden_size, gate_size)).astype(np.float16) self.w_list.append(Parameter(initializer(Tensor(w_bw_np), [w_shape + hidden_size, gate_size]), name='weight_bw' + str(layer))) b_bw_np = np.random.uniform(-stdv, stdv, (4 * hidden_size)).astype(np.float16) if has_bias else b0 self.b_list.append(Parameter(initializer(Tensor(b_bw_np), [gate_size]), name='bias_bw' + str(layer))) self.w_list = ParameterTuple(self.w_list) self.b_list = ParameterTuple(self.b_list) else: for layer in range(num_layers): input_layer_size = input_size if layer == 0 else hidden_size * num_directions increment_size = gate_size * input_layer_size increment_size += gate_size * hidden_size if has_bias: increment_size += 2 * gate_size weight_size += increment_size * num_directions w_np = np.random.uniform(-stdv, stdv, (weight_size, 1, 1)).astype(np.float32) self.weight = Parameter(initializer(Tensor(w_np), [weight_size, 1, 1]), name='weight') def _stacked_bi_dynamic_rnn(self, x, init_h, init_c, weight, bias): """stacked bidirectional dynamic_rnn""" x_shape = self.shape(x) sequence_length = _create_sequence_length(x_shape) pre_layer = x hn = () cn = () output = x for i in range(self.num_layers): offset = i * 2 weight_fw, weight_bw = weight[offset], weight[offset + 1] bias_fw, bias_bw = bias[offset], bias[offset + 1] init_h_fw, init_h_bw = init_h[offset:offset + 1, :, :], init_h[offset + 1:offset + 2, :, :] init_c_fw, init_c_bw = init_c[offset:offset + 1, :, :], init_c[offset + 1:offset + 2, :, :] bw_x = self.reverse_seq(pre_layer, sequence_length) y, h, c, _, _, _, _, _ = self.rnns_fw(pre_layer, weight_fw, bias_fw, None, init_h_fw, init_c_fw) y_bw, h_bw, c_bw, _, _, _, _, _ = self.rnns_bw(bw_x, weight_bw, bias_bw, None, init_h_bw, init_c_bw) y_bw = self.reverse_seq(y_bw, sequence_length) output = self.concat_2dim((y, y_bw)) pre_layer = self.dropout_op(output) if self.dropout else output hn += (h[-1:, :, :],) hn += (h_bw[-1:, :, :],) cn += (c[-1:, :, :],) cn += (c_bw[-1:, :, :],) status_h = self.concat(hn) status_c = self.concat(cn) return output, status_h, status_c def _stacked_dynamic_rnn(self, x, init_h, init_c, weight, bias): """stacked mutil_layer dynamic_rnn""" pre_layer = x hn = () cn = () y = 0 for i in range(self.num_layers): weight_fw, bias_bw = weight[i], bias[i] init_h_fw, init_c_bw = init_h[i:i + 1, :, :], init_c[i:i + 1, :, :] y, h, c, _, _, _, _, _ = self.rnns_fw(pre_layer, weight_fw, bias_bw, None, init_h_fw, init_c_bw) pre_layer = self.dropout_op(y) if self.dropout else y hn += (h[-1:, :, :],) cn += (c[-1:, :, :],) status_h = self.concat(hn) status_c = self.concat(cn) return y, status_h, status_c def construct(self, x, hx): if self.batch_first: x = self.transpose(x, (1, 0, 2)) h, c = hx if self.is_ascend: x_dtype = F.dtype(x) h_dtype = F.dtype(h) c_dtype = F.dtype(c) _check_input_3d(F.shape(h), "h of hx", self.cls_name) _check_input_3d(F.shape(c), "c of hx", self.cls_name) _check_input_dtype(x_dtype, "x", [mstype.float32, mstype.float16], self.cls_name) _check_input_dtype(h_dtype, "h", [mstype.float32, mstype.float16], self.cls_name) _check_input_dtype(c_dtype, "c", [mstype.float32, mstype.float16], self.cls_name) x = self.cast(x, mstype.float16) h = self.cast(h, mstype.float16) c = self.cast(c, mstype.float16) if self.bidirectional: x, h, c = self._stacked_bi_dynamic_rnn(x, h, c, self.w_list, self.b_list) else: x, h, c = self._stacked_dynamic_rnn(x, h, c, self.w_list, self.b_list) x = self.cast(x, x_dtype) h = self.cast(h, h_dtype) c = self.cast(c, c_dtype) else: x, h, c, _, _ = self.lstm(x, h, c, self.weight) if self.batch_first: x = self.transpose(x, (1, 0, 2)) return x, (h, c)
[docs]class LSTMCell(Cell): r""" LSTM (Long Short-Term Memory) layer. Apply LSTM layer to the input. There are two pipelines connecting two consecutive cells in a LSTM model; one is cell state pipeline and the other is hidden state pipeline. Denote two consecutive time nodes as :math:`t-1` and :math:`t`. Given an input :math:`x_t` at time :math:`t`, an hidden state :math:`h_{t-1}` and an cell state :math:`c_{t-1}` of the layer at time :math:`{t-1}`, the cell state and hidden state at time :math:`t` is computed using an gating mechanism. Input gate :math:`i_t` is designed to protect the cell from perturbation by irrelevant inputs. Forget gate :math:`f_t` affords protection of the cell by forgetting some information in the past, which is stored in :math:`h_{t-1}`. Output gate :math:`o_t` protects other units from perturbation by currently irrelevant memory contents. Candidate cell state :math:`\tilde{c}_t` is calculated with the current input, on which the input gate will be applied. Finally, current cell state :math:`c_{t}` and hidden state :math:`h_{t}` are computed with the calculated gates and cell states. The complete formulation is as follows. .. math:: \begin{array}{ll} \\ i_t = \sigma(W_{ix} x_t + b_{ix} + W_{ih} h_{(t-1)} + b_{ih}) \\ f_t = \sigma(W_{fx} x_t + b_{fx} + W_{fh} h_{(t-1)} + b_{fh}) \\ \tilde{c}_t = \tanh(W_{cx} x_t + b_{cx} + W_{ch} h_{(t-1)} + b_{ch}) \\ o_t = \sigma(W_{ox} x_t + b_{ox} + W_{oh} h_{(t-1)} + b_{oh}) \\ c_t = f_t * c_{(t-1)} + i_t * \tilde{c}_t \\ h_t = o_t * \tanh(c_t) \\ \end{array} Here :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product. :math:`W, b` are learnable weights between the output and the input in the formula. For instance, :math:`W_{ix}, b_{ix}` are the weight and bias used to transform from input :math:`x` to :math:`i`. Details can be found in paper `LONG SHORT-TERM MEMORY <https://www.bioinf.jku.at/publications/older/2604.pdf>`_ and `Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling <https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43905.pdf>`_. LSTMCell is a single-layer RNN, you can achieve multi-layer RNN by stacking LSTMCell. Args: input_size (int): Number of features of input. hidden_size (int): Number of features of hidden layer. has_bias (bool): Whether the cell has bias `b_ih` and `b_hh`. Default: True. batch_first (bool): Specifies whether the first dimension of input is batch_size. Default: False. dropout (float, int): If not 0, append `Dropout` layer on the outputs of each LSTM layer except the last layer. Default 0. The range of dropout is [0.0, 1.0]. bidirectional (bool): Specifies whether this is a bidirectional LSTM. If set True, number of directions will be 2 otherwise number of directions is 1. Default: False. Inputs: - **input** (Tensor) - Tensor of shape (seq_len, batch_size, `input_size`). - **h** - data type mindspore.float32 or mindspore.float16 and shape (num_directions, batch_size, `hidden_size`). - **c** - data type mindspore.float32 or mindspore.float16 and shape (num_directions, batch_size, `hidden_size`). Data type of `h' and 'c' must be the same of `input`. - **w** - data type mindspore.float32 or mindspore.float16 and shape (`weight_size`, 1, 1). The value of `weight_size` depends on `input_size`, `hidden_size` and `bidirectional` Outputs: `output`, `h_n`, `c_n`, 'reserve', 'state'. - **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`). - **h** - A Tensor with shape (num_directions, batch_size, `hidden_size`). - **c** - A Tensor with shape (num_directions, batch_size, `hidden_size`). - **reserve** - reserved - **state** - reserved Raises: TypeError: If `input_size` or `hidden_size` or `num_layers` is not an int. TypeError: If `has_bias` or `batch_first` or `bidirectional` is not a bool. TypeError: If `dropout` is neither a float nor an int. ValueError: If `dropout` is not in range [0.0, 1.0]. Supported Platforms: ``GPU`` ``CPU`` Examples: >>> net = nn.LSTMCell(10, 12, has_bias=True, batch_first=True, bidirectional=False) >>> input = Tensor(np.ones([3, 5, 10]).astype(np.float32)) >>> h = Tensor(np.ones([1, 3, 12]).astype(np.float32)) >>> c = Tensor(np.ones([1, 3, 12]).astype(np.float32)) >>> w = Tensor(np.ones([1152, 1, 1]).astype(np.float32)) >>> output, h, c, _, _ = net(input, h, c, w) >>> print(output.shape) (3, 5, 12) """ def __init__(self, input_size, hidden_size, has_bias=True, batch_first=False, dropout=0, bidirectional=False): super(LSTMCell, self).__init__() self.batch_first = validator.check_value_type("batch_first", batch_first, [bool], self.cls_name) self.transpose = P.Transpose() self.lstm = P.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=1, has_bias=has_bias, bidirectional=bidirectional, dropout=float(dropout)) def construct(self, x, h, c, w): if self.batch_first: x = self.transpose(x, (1, 0, 2)) x, h, c, _, _ = self.lstm(x, h, c, w) if self.batch_first: x = self.transpose(x, (1, 0, 2)) return x, h, c, _, _