Class: Torch::NN::RNNBase

Inherits:
Module
  • Object
show all
Defined in:
lib/torch/nn/rnn_base.rb

Direct Known Subclasses

GRU, LSTM, RNN

Instance Attribute Summary

Attributes inherited from Module

#training

Instance Method Summary collapse

Methods inherited from Module

#add_module, #apply, #buffers, #call, #children, #cpu, #cuda, #deep_dup, #double, #eval, #float, #half, #inspect, #load_state_dict, #method_missing, #modules, #named_buffers, #named_children, #named_modules, #named_parameters, #parameters, #register_buffer, #register_parameter, #requires_grad!, #respond_to?, #share_memory, #state_dict, #to, #train, #type, #zero_grad

Methods included from Utils

#_activation_fn, #_clones, #_ntuple, #_pair, #_quadrupal, #_single, #_triple

Constructor Details

#initialize(mode, input_size, hidden_size, num_layers: 1, bias: true, batch_first: false, dropout: 0.0, bidirectional: false) ⇒ RNNBase

Returns a new instance of RNNBase.



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# File 'lib/torch/nn/rnn_base.rb', line 4

def initialize(
  mode,
  input_size,
  hidden_size,
  num_layers: 1,
  bias: true,
  batch_first: false,
  dropout: 0.0,
  bidirectional: false
)
  super()
  @mode = mode
  @input_size = input_size
  @hidden_size = hidden_size
  @num_layers = num_layers
  @bias = bias
  @batch_first = batch_first
  @dropout = dropout.to_f
  @bidirectional = bidirectional
  num_directions = bidirectional ? 2 : 1

  if !dropout.is_a?(Numeric) || !(dropout >= 0 && dropout <= 1)
    raise ArgumentError, "dropout should be a number in range [0, 1] " +
                         "representing the probability of an element being " +
                         "zeroed"
  end
  if dropout > 0 && num_layers == 1
    warn "dropout option adds dropout after all but last " +
         "recurrent layer, so non-zero dropout expects " +
         "num_layers greater than 1, but got dropout=#{dropout} and " +
         "num_layers=#{num_layers}"
  end

  gate_size =
    case mode
    when "LSTM"
      4 * hidden_size
    when "GRU"
      3 * hidden_size
    when "RNN_TANH"
      hidden_size
    when "RNN_RELU"
      hidden_size
    else
      raise ArgumentError, "Unrecognized RNN mode: #{mode}"
    end

  @all_weights = []
  num_layers.times do |layer|
    num_directions.times do |direction|
      layer_input_size = layer == 0 ? input_size : hidden_size * num_directions

      w_ih = Parameter.new(Torch::Tensor.new(gate_size, layer_input_size))
      w_hh = Parameter.new(Torch::Tensor.new(gate_size, hidden_size))
      b_ih = Parameter.new(Torch::Tensor.new(gate_size))
      # Second bias vector included for CuDNN compatibility. Only one
      # bias vector is needed in standard definition.
      b_hh = Parameter.new(Torch::Tensor.new(gate_size))
      layer_params = [w_ih, w_hh, b_ih, b_hh]

      suffix = direction == 1 ? "_reverse" : ""
      param_names = ["weight_ih_l%s%s", "weight_hh_l%s%s"]
      if bias
        param_names += ["bias_ih_l%s%s", "bias_hh_l%s%s"]
      end
      param_names.map! { |x| x % [layer, suffix] }

      param_names.zip(layer_params) do |name, param|
        instance_variable_set("@#{name}", param)
      end
      @all_weights << param_names
    end
  end

  flatten_parameters
  reset_parameters
end

Dynamic Method Handling

This class handles dynamic methods through the method_missing method in the class Torch::NN::Module

Instance Method Details

#_apply(fn) ⇒ Object 



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# File 'lib/torch/nn/rnn_base.rb', line 86

def _apply(fn)
  ret = super
  flatten_parameters
  ret
end

#extra_inspect ⇒ Object

TODO add more parameters



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# File 'lib/torch/nn/rnn_base.rb', line 153

def extra_inspect
  s = String.new("%{input_size}, %{hidden_size}")
  if @num_layers != 1
    s += ", num_layers: %{num_layers}"
  end
  format(s, input_size: @input_size, hidden_size: @hidden_size, num_layers: @num_layers)
end

#flatten_parameters ⇒ Object 



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# File 'lib/torch/nn/rnn_base.rb', line 82

def flatten_parameters
  # no-op unless module is on the GPU and cuDNN is enabled
end

#forward(input, hx: nil) ⇒ Object 



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# File 'lib/torch/nn/rnn_base.rb', line 106

def forward(input, hx: nil)
  is_packed = false # TODO isinstance(input, PackedSequence)
  if is_packed
    input, batch_sizes, sorted_indices, unsorted_indices = input
    max_batch_size = batch_sizes[0]
    max_batch_size = max_batch_size.to_i
  else
    batch_sizes = nil
    max_batch_size = @batch_first ? input.size(0) : input.size(1)
    sorted_indices = nil
    unsorted_indices = nil
  end

  if hx.nil?
    num_directions = @bidirectional ? 2 : 1
    hx = Torch.zeros(@num_layers * num_directions, max_batch_size,
      @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 = permute_hidden(hx, sorted_indices)
  end

  check_forward_args(input, hx, batch_sizes)
  _rnn_impls = {
    "RNN_TANH" => Torch.method(:rnn_tanh),
    "RNN_RELU" => Torch.method(:rnn_relu)
  }
  _impl = _rnn_impls[@mode]
  if batch_sizes.nil?
    result = _impl.call(input, hx, _get_flat_weights, @bias, @num_layers,
                     @dropout, @training, @bidirectional, @batch_first)
  else
    result = _impl.call(input, batch_sizes, hx, _get_flat_weights, @bias,
                     @num_layers, @dropout, @training, @bidirectional)
  end
  output = result[0]
  hidden = result[1]

  if is_packed
    raise NotImplementedYet
    # output = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
  end
  [output, permute_hidden(hidden, unsorted_indices)]
end

#permute_hidden(hx, permutation) ⇒ Object

Raises:



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# File 'lib/torch/nn/rnn_base.rb', line 99

def permute_hidden(hx, permutation)
  if permutation.nil?
    return hx
  end
  raise NotImplementedYet
end

#reset_parameters ⇒ Object 



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# File 'lib/torch/nn/rnn_base.rb', line 92

def reset_parameters
  stdv = 1.0 / Math.sqrt(@hidden_size)
  parameters.each do |weight|
    Init.uniform!(weight, a: -stdv, b: stdv)
  end
end