From 1ed691459980004f0f9e53bd19d751fa5449d79f Mon Sep 17 00:00:00 2001
From: Oleksii Kuchaiev <okuchaiev@users.noreply.github.com>
Date: Thu, 4 May 2017 09:44:57 -0700
Subject: [PATCH] Group LSTM cell (#9606)
* GLSTM cell from https://openreview.net/forum?id=ByxWXyNFg¬eId=ByxWXyNFg
* Responding to comments on PR#9606
* Update comments according to review.
* More fixes on users' behalf.
---
.../rnn/python/kernel_tests/rnn_cell_test.py | 58 ++++++
tensorflow/contrib/rnn/python/ops/rnn_cell.py | 178 ++++++++++++++++++
2 files changed, 236 insertions(+)
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
index 55fd7e7a51b..31ac89b4a84 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
@@ -906,6 +906,64 @@ class RNNCellTest(test.TestCase):
# States are left untouched
self.assertAllClose(res[2], res[3])
+ def testGLSTMCell(self):
+ # Ensure that G-LSTM matches LSTM when number_of_groups = 1
+ batch_size = 2
+ num_units = 4
+ number_of_groups = 1
+
+ with self.test_session() as sess:
+ with variable_scope.variable_scope(
+ "root1", initializer=init_ops.constant_initializer(0.5)):
+ x = array_ops.ones([batch_size, num_units])
+ # When number_of_groups = 1, G-LSTM is equivalent to regular LSTM
+ gcell = rnn_cell.GLSTMCell(num_units=num_units,
+ number_of_groups=number_of_groups)
+ cell = core_rnn_cell_impl.LSTMCell(num_units=num_units)
+ self.assertTrue(isinstance(gcell.state_size, tuple))
+ zero_state = gcell.zero_state(batch_size=batch_size,
+ dtype=dtypes.float32)
+ gh, gs = gcell(x, zero_state)
+ h, g = cell(x, zero_state)
+
+ sess.run([variables.global_variables_initializer()])
+ glstm_result = sess.run([gh, gs])
+ lstm_result = sess.run([h, g])
+
+ self.assertAllClose(glstm_result[0], lstm_result[0], 1e-5)
+ self.assertAllClose(glstm_result[1], lstm_result[1], 1e-5)
+
+ # Test that G-LSTM subgroup act like corresponding sub-LSTMs
+ batch_size = 2
+ num_units = 4
+ number_of_groups = 2
+
+ with self.test_session() as sess:
+ with variable_scope.variable_scope(
+ "root2", initializer=init_ops.constant_initializer(0.5)):
+ # input for G-LSTM with 2 groups
+ glstm_input = array_ops.ones([batch_size, num_units])
+ gcell = rnn_cell.GLSTMCell(num_units=num_units,
+ number_of_groups=number_of_groups)
+ gcell_zero_state = gcell.zero_state(batch_size=batch_size,
+ dtype=dtypes.float32)
+ gh, gs = gcell(glstm_input, gcell_zero_state)
+
+ # input for LSTM cell simulating single G-LSTM group
+ lstm_input = array_ops.ones([batch_size, num_units / number_of_groups])
+ # note division by number_of_groups. This cell one simulates G-LSTM group
+ cell = core_rnn_cell_impl.LSTMCell(num_units=
+ int(num_units / number_of_groups))
+ cell_zero_state = cell.zero_state(batch_size=batch_size,
+ dtype=dtypes.float32)
+ h, g = cell(lstm_input, cell_zero_state)
+
+ sess.run([variables.global_variables_initializer()])
+ [gh_res, h_res] = sess.run([gh, h])
+ self.assertAllClose(gh_res[:, 0:int(num_units / number_of_groups)],
+ h_res, 1e-5)
+ self.assertAllClose(gh_res[:, int(num_units / number_of_groups):],
+ h_res, 1e-5)
class LayerNormBasicLSTMCellTest(test.TestCase):
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index acba77f0e13..df36dd2bf9b 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -1926,3 +1926,181 @@ class PhasedLSTMCell(core_rnn_cell.RNNCell):
new_state = core_rnn_cell.LSTMStateTuple(new_c, new_h)
return new_h, new_state
+
+
+class GLSTMCell(core_rnn_cell.RNNCell):
+ """Group LSTM cell (G-LSTM).
+
+ The implementation is based on:
+
+ https://arxiv.org/abs/1703.10722
+
+ O. Kuchaiev and B. Ginsburg
+ "Factorization Tricks for LSTM Networks", ICLR 2017 workshop.
+ """
+
+ def __init__(self, num_units, initializer=None, num_proj=None,
+ number_of_groups=1, forget_bias=1.0, activation=math_ops.tanh,
+ reuse=None):
+ """Initialize the parameters of G-LSTM cell.
+
+ Args:
+ num_units: int, The number of units in the G-LSTM cell
+ initializer: (optional) The initializer to use for the weight and
+ projection matrices.
+ num_proj: (optional) int, The output dimensionality for the projection
+ matrices. If None, no projection is performed.
+ number_of_groups: (optional) int, number of groups to use.
+ If `number_of_groups` is 1, then it should be equivalent to LSTM cell
+ forget_bias: Biases of the forget gate are initialized by default to 1
+ in order to reduce the scale of forgetting at the beginning of
+ the training.
+ activation: Activation function of the inner states.
+ reuse: (optional) Python boolean describing whether to reuse variables
+ in an existing scope. If not `True`, and the existing scope already
+ has the given variables, an error is raised.
+
+ Raises:
+ ValueError: If `num_units` or `num_proj` is not divisible by
+ `number_of_groups`.
+ """
+ super(GLSTMCell, self).__init__(_reuse=reuse)
+ self._num_units = num_units
+ self._initializer = initializer
+ self._num_proj = num_proj
+ self._forget_bias = forget_bias
+ self._activation = activation
+ self._number_of_groups = number_of_groups
+
+ if self._num_units % self._number_of_groups != 0:
+ raise ValueError("num_units must be divisible by number_of_groups")
+ if self._num_proj:
+ if self._num_proj % self._number_of_groups != 0:
+ raise ValueError("num_proj must be divisible by number_of_groups")
+ self._group_shape = [int(self._num_proj / self._number_of_groups),
+ int(self._num_units / self._number_of_groups)]
+ else:
+ self._group_shape = [int(self._num_units / self._number_of_groups),
+ int(self._num_units / self._number_of_groups)]
+
+ if num_proj:
+ self._state_size = core_rnn_cell.LSTMStateTuple(num_units, num_proj)
+ self._output_size = num_proj
+ else:
+ self._state_size = core_rnn_cell.LSTMStateTuple(num_units, num_units)
+ self._output_size = num_units
+
+ @property
+ def state_size(self):
+ return self._state_size
+
+ @property
+ def output_size(self):
+ return self._output_size
+
+ def _get_input_for_group(self, inputs, group_id, group_size):
+ """Slices inputs into groups to prepare for processing by cell's groups
+
+ Args:
+ inputs: cell input or it's previous state,
+ a Tensor, 2D, [batch x num_units]
+ group_id: group id, a Scalar, for which to prepare input
+ group_size: size of the group
+
+ Returns:
+ subset of inputs corresponding to group "group_id",
+ a Tensor, 2D, [batch x num_units/number_of_groups]
+ """
+ batch_size = inputs.shape[0].value or array_ops.shape(value)[0]
+ return array_ops.slice(input_=inputs,
+ begin=[0, group_id * group_size],
+ size=[batch_size, group_size],
+ name=("GLSTM_group%d_input_generation" % group_id))
+
+ def call(self, inputs, state):
+ """Run one step of G-LSTM.
+
+ Args:
+ inputs: input Tensor, 2D, [batch x num_units].
+ state: this must be a tuple of state Tensors, both `2-D`,
+ with column sizes `c_state` and `m_state`.
+
+ Returns:
+ A tuple containing:
+
+ - A `2-D, [batch x output_dim]`, Tensor representing the output of the
+ G-LSTM after reading `inputs` when previous state was `state`.
+ Here output_dim is:
+ num_proj if num_proj was set,
+ num_units otherwise.
+ - LSTMStateTuple representing the new state of G-LSTM cell
+ after reading `inputs` when the previous state was `state`.
+
+ Raises:
+ ValueError: If input size cannot be inferred from inputs via
+ static shape inference.
+ """
+ (c_prev, m_prev) = state
+
+ input_size = inputs.get_shape().with_rank(2)[1]
+ if input_size.value is None:
+ raise ValueError("Couldn't infer input size from inputs.get_shape()[-1]")
+ dtype = inputs.dtype
+ scope = vs.get_variable_scope()
+ with vs.variable_scope(scope, initializer=self._initializer):
+ i_parts = []
+ j_parts = []
+ f_parts = []
+ o_parts = []
+
+ for group_id in range(self._number_of_groups):
+ with vs.variable_scope("group%d" % group_id):
+ x_g_id = array_ops.concat(
+ [self._get_input_for_group(inputs, group_id,
+ self._group_shape[0]),
+ self._get_input_for_group(m_prev, group_id,
+ self._group_shape[0])], axis=1)
+ R_k = _linear(x_g_id, 4 * self._group_shape[1], bias=False)
+ i_k, j_k, f_k, o_k = array_ops.split(R_k, 4, 1)
+
+ i_parts.append(i_k)
+ j_parts.append(j_k)
+ f_parts.append(f_k)
+ o_parts.append(o_k)
+
+ bi = vs.get_variable(name="bias_i",
+ shape=[self._num_units],
+ dtype=dtype,
+ initializer=
+ init_ops.constant_initializer(0.0, dtype=dtype))
+ bj = vs.get_variable(name="bias_j",
+ shape=[self._num_units],
+ dtype=dtype,
+ initializer=
+ init_ops.constant_initializer(0.0, dtype=dtype))
+ bf = vs.get_variable(name="bias_f",
+ shape=[self._num_units],
+ dtype=dtype,
+ initializer=
+ init_ops.constant_initializer(0.0, dtype=dtype))
+ bo = vs.get_variable(name="bias_o",
+ shape=[self._num_units],
+ dtype=dtype,
+ initializer=
+ init_ops.constant_initializer(0.0, dtype=dtype))
+
+ i = nn_ops.bias_add(array_ops.concat(i_parts, axis=1), bi)
+ j = nn_ops.bias_add(array_ops.concat(j_parts, axis=1), bj)
+ f = nn_ops.bias_add(array_ops.concat(f_parts, axis=1), bf)
+ o = nn_ops.bias_add(array_ops.concat(o_parts, axis=1), bo)
+
+ c = (math_ops.sigmoid(f + self._forget_bias) * c_prev +
+ math_ops.sigmoid(i) * math_ops.tanh(j))
+ m = math_ops.sigmoid(o) * self._activation(c)
+
+ if self._num_proj is not None:
+ with vs.variable_scope("projection"):
+ m = _linear(m, self._num_proj, bias=False)
+
+ new_state = core_rnn_cell.LSTMStateTuple(c, m)
+ return m, new_state