--- a
+++ b/util/LSTM.lua
@@ -0,0 +1,284 @@
+require 'torch'
+require 'nn'
+
+
+local layer, parent = torch.class('nn.LSTM', 'nn.Module')
+
+-- Implemented from https://github.com/jcjohnson/torch-rnn
+
+function layer:__init(input_dim, hidden_dim)
+  parent.__init(self)
+
+  local D, H = input_dim, hidden_dim
+  self.input_dim, self.hidden_dim = D, H
+
+  self.weight = torch.Tensor(D + H, 4 * H)
+  self.gradWeight = torch.Tensor(D + H, 4 * H):zero()
+  self.bias = torch.Tensor(4 * H)
+  self.gradBias = torch.Tensor(4 * H):zero()
+  self:reset()
+
+  self.cell = torch.Tensor()    -- This will be (N, T, H)
+  self.gates = torch.Tensor()   -- This will be (N, T, 4H)
+  self.buffer1 = torch.Tensor() -- This will be (N, H)
+  self.buffer2 = torch.Tensor() -- This will be (N, H)
+  self.buffer3 = torch.Tensor() -- This will be (1, 4H)
+  self.grad_a_buffer = torch.Tensor() -- This will be (N, 4H)
+
+  self.h0 = torch.Tensor()
+  self.c0 = torch.Tensor()
+  self.remember_states = false
+
+  self.grad_c0 = torch.Tensor()
+  self.grad_h0 = torch.Tensor()
+  self.grad_x = torch.Tensor()
+  self.gradInput = {self.grad_c0, self.grad_h0, self.grad_x}
+end
+
+
+function layer:reset(std)
+  if not std then
+    std = 1.0 / math.sqrt(self.hidden_dim + self.input_dim)
+  end
+  self.bias:zero()
+  self.bias[{{self.hidden_dim + 1, 2 * self.hidden_dim}}]:fill(1)
+  self.weight:normal(0, std)
+  return self
+end
+
+
+function layer:resetStates()
+  self.h0 = self.h0.new()
+  self.c0 = self.c0.new()
+end
+
+
+local function check_dims(x, dims)
+  assert(x:dim() == #dims)
+  for i, d in ipairs(dims) do
+    assert(x:size(i) == d)
+  end
+end
+
+
+function layer:_unpack_input(input)
+  local c0, h0, x = nil, nil, nil
+  if torch.type(input) == 'table' and #input == 3 then
+    c0, h0, x = unpack(input)
+  elseif torch.type(input) == 'table' and #input == 2 then
+    h0, x = unpack(input)
+  elseif torch.isTensor(input) then
+    x = input
+  else
+    assert(false, 'invalid input')
+  end
+  return c0, h0, x
+end
+
+
+function layer:_get_sizes(input, gradOutput)
+  local c0, h0, x = self:_unpack_input(input)
+  local N, T = x:size(1), x:size(2)
+  local H, D = self.hidden_dim, self.input_dim
+  check_dims(x, {N, T, D})
+  if h0 then
+    check_dims(h0, {N, H})
+  end
+  if c0 then
+    check_dims(c0, {N, H})
+  end
+  if gradOutput then
+    check_dims(gradOutput, {N, T, H})
+  end
+  return N, T, D, H
+end
+
+
+--[[
+Input:
+- c0: Initial cell state, (N, H)
+- h0: Initial hidden state, (N, H)
+- x: Input sequence, (N, T, D)
+
+Output:
+- h: Sequence of hidden states, (N, T, H)
+--]]
+function layer:updateOutput(input)
+  self.recompute_backward = true
+  local c0, h0, x = self:_unpack_input(input)
+  local N, T, D, H = self:_get_sizes(input)
+
+  self._return_grad_c0 = (c0 ~= nil)
+  self._return_grad_h0 = (h0 ~= nil)
+  if not c0 then
+    c0 = self.c0
+    if c0:nElement() == 0 or not self.remember_states then
+      c0:resize(N, H):zero()
+    elseif self.remember_states then
+      local prev_N, prev_T = self.cell:size(1), self.cell:size(2)
+      assert(prev_N == N, 'batch sizes must be constant to remember states')
+      c0:copy(self.cell[{{}, prev_T}])
+    end
+  end
+  if not h0 then
+    h0 = self.h0
+    if h0:nElement() == 0 or not self.remember_states then
+      h0:resize(N, H):zero()
+    elseif self.remember_states then
+      local prev_N, prev_T = self.output:size(1), self.output:size(2)
+      assert(prev_N == N, 'batch sizes must be the same to remember states')
+      h0:copy(self.output[{{}, prev_T}])
+    end
+  end
+
+  local bias_expand = self.bias:view(1, 4 * H):expand(N, 4 * H)
+  local Wx = self.weight[{{1, D}}]
+  local Wh = self.weight[{{D + 1, D + H}}]
+
+  local h, c = self.output, self.cell
+  h:resize(N, T, H):zero()
+  c:resize(N, T, H):zero()
+  local prev_h, prev_c = h0, c0
+  self.gates:resize(N, T, 4 * H):zero()
+  for t = 1, T do
+    local cur_x = x[{{}, t}]
+    local next_h = h[{{}, t}]
+    local next_c = c[{{}, t}]
+    local cur_gates = self.gates[{{}, t}]
+    cur_gates:addmm(bias_expand, cur_x, Wx)
+    cur_gates:addmm(prev_h, Wh)
+    cur_gates[{{}, {1, 3 * H}}]:sigmoid()
+    cur_gates[{{}, {3 * H + 1, 4 * H}}]:tanh()
+    local i = cur_gates[{{}, {1, H}}]
+    local f = cur_gates[{{}, {H + 1, 2 * H}}]
+    local o = cur_gates[{{}, {2 * H + 1, 3 * H}}]
+    local g = cur_gates[{{}, {3 * H + 1, 4 * H}}]
+    next_h:cmul(i, g)
+    next_c:cmul(f, prev_c):add(next_h)
+    next_h:tanh(next_c):cmul(o)
+    prev_h, prev_c = next_h, next_c
+  end
+
+  return self.output
+end
+
+
+function layer:backward(input, gradOutput, scale)
+  self.recompute_backward = false
+  scale = scale or 1.0
+  assert(scale == 1.0, 'must have scale=1')
+  local c0, h0, x = self:_unpack_input(input)
+  if not c0 then c0 = self.c0 end
+  if not h0 then h0 = self.h0 end
+
+  local grad_c0, grad_h0, grad_x = self.grad_c0, self.grad_h0, self.grad_x
+  local h, c = self.output, self.cell
+  local grad_h = gradOutput
+
+  local N, T, D, H = self:_get_sizes(input, gradOutput)
+  local Wx = self.weight[{{1, D}}]
+  local Wh = self.weight[{{D + 1, D + H}}]
+  local grad_Wx = self.gradWeight[{{1, D}}]
+  local grad_Wh = self.gradWeight[{{D + 1, D + H}}]
+  local grad_b = self.gradBias
+
+  grad_h0:resizeAs(h0):zero()
+  grad_c0:resizeAs(c0):zero()
+  grad_x:resizeAs(x):zero()
+  local grad_next_h = self.buffer1:resizeAs(h0):zero()
+  local grad_next_c = self.buffer2:resizeAs(c0):zero()
+  for t = T, 1, -1 do
+    local next_h, next_c = h[{{}, t}], c[{{}, t}]
+    local prev_h, prev_c = nil, nil
+    if t == 1 then
+      prev_h, prev_c = h0, c0
+    else
+      prev_h, prev_c = h[{{}, t - 1}], c[{{}, t - 1}]
+    end
+    grad_next_h:add(grad_h[{{}, t}])
+
+    local i = self.gates[{{}, t, {1, H}}]
+    local f = self.gates[{{}, t, {H + 1, 2 * H}}]
+    local o = self.gates[{{}, t, {2 * H + 1, 3 * H}}]
+    local g = self.gates[{{}, t, {3 * H + 1, 4 * H}}]
+
+    local grad_a = self.grad_a_buffer:resize(N, 4 * H):zero()
+    local grad_ai = grad_a[{{}, {1, H}}]
+    local grad_af = grad_a[{{}, {H + 1, 2 * H}}]
+    local grad_ao = grad_a[{{}, {2 * H + 1, 3 * H}}]
+    local grad_ag = grad_a[{{}, {3 * H + 1, 4 * H}}]
+
+    -- We will use grad_ai, grad_af, and grad_ao as temporary buffers
+    -- to to compute grad_next_c. We will need tanh_next_c (stored in grad_ai)
+    -- to compute grad_ao; the other values can be overwritten after we compute
+    -- grad_next_c
+    local tanh_next_c = grad_ai:tanh(next_c)
+    local tanh_next_c2 = grad_af:cmul(tanh_next_c, tanh_next_c)
+    local my_grad_next_c = grad_ao
+    my_grad_next_c:fill(1):add(-1, tanh_next_c2):cmul(o):cmul(grad_next_h)
+    grad_next_c:add(my_grad_next_c)
+
+    -- We need tanh_next_c (currently in grad_ai) to compute grad_ao; after
+    -- that we can overwrite it.
+    grad_ao:fill(1):add(-1, o):cmul(o):cmul(tanh_next_c):cmul(grad_next_h)
+
+    -- Use grad_ai as a temporary buffer for computing grad_ag
+    local g2 = grad_ai:cmul(g, g)
+    grad_ag:fill(1):add(-1, g2):cmul(i):cmul(grad_next_c)
+
+    -- We don't need any temporary storage for these so do them last
+    grad_ai:fill(1):add(-1, i):cmul(i):cmul(g):cmul(grad_next_c)
+    grad_af:fill(1):add(-1, f):cmul(f):cmul(prev_c):cmul(grad_next_c)
+
+    grad_x[{{}, t}]:mm(grad_a, Wx:t())
+    grad_Wx:addmm(scale, x[{{}, t}]:t(), grad_a)
+    grad_Wh:addmm(scale, prev_h:t(), grad_a)
+    local grad_a_sum = self.buffer3:resize(1, 4 * H):sum(grad_a, 1)
+    grad_b:add(scale, grad_a_sum)
+
+    grad_next_h:mm(grad_a, Wh:t())
+    grad_next_c:cmul(f)
+  end
+  grad_h0:copy(grad_next_h)
+  grad_c0:copy(grad_next_c)
+
+  if self._return_grad_c0 and self._return_grad_h0 then
+    self.gradInput = {self.grad_c0, self.grad_h0, self.grad_x}
+  elseif self._return_grad_h0 then
+    self.gradInput = {self.grad_h0, self.grad_x}
+  else
+    self.gradInput = self.grad_x
+  end
+
+  return self.gradInput
+end
+
+
+function layer:clearState()
+  self.cell:set()
+  self.gates:set()
+  self.buffer1:set()
+  self.buffer2:set()
+  self.buffer3:set()
+  self.grad_a_buffer:set()
+
+  self.grad_c0:set()
+  self.grad_h0:set()
+  self.grad_x:set()
+  self.output:set()
+end
+
+
+function layer:updateGradInput(input, gradOutput)
+  if self.recompute_backward then
+    self:backward(input, gradOutput, 1.0)
+  end
+  return self.gradInput
+end
+
+
+function layer:accGradParameters(input, gradOutput, scale)
+  if self.recompute_backward then
+    self:backward(input, gradOutput, scale)
+  end
+end