Merge branch 'develop' into prune_ranks

.travis.yml

@@ -12,6 +12,8 @@ env:
     - TF_VERSION=1.2
     - TF_VERSION=1.3
     - TF_VERSION=1.4
+    - TF_VERSION=1.6
+    - TF_VERSION=1.7
 # command to install dependencies
 install:
   # Python 2.7 has very old pip by default that doesn't have tensorflow.

README.md

@@ -7,7 +7,7 @@ TensorFlow implementation of the Tensor Train (TT) -Toolbox.
 API is available via [readthedocs](https://t3f.readthedocs.io/en/latest/).
 
 # Installation
-T3f assumes you have a working TensorFlow installation, supported versions are from 1.0 to 1.4 (see [here](https://www.tensorflow.org/versions/r1.4/install/) for TF 1.4 installation instructions).
+T3f assumes you have Python 2.7, 3.4, 3.5, or 3.6 and a working TensorFlow installation, supported versions are from 1.0 to 1.7 (see [here](https://www.tensorflow.org/versions/r1.7/install/) for TF 1.7 installation instructions).
 We don't include it into pip requirements since the installation of TensorFlow varies depending on your setup.
 Then simply run
 ```bash

docs/index.rst

@@ -4,7 +4,7 @@
    contain the root `toctree` directive.
 
 Welcome to t3f documentation!
-===============================
+=============================
 
 Module contents
 ---------------
@@ -24,10 +24,17 @@ t3f\.utils module
     :show-inheritance:
 
 t3f\.kronecker module
------------------
+---------------------
 
 .. automodule:: t3f.kronecker
     :members:
     :undoc-members:
     :show-inheritance:
 
+t3f\.approximate module
+-----------------------
+
+.. automodule:: t3f.approximate
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/tf_requirement.txt

@@ -4,4 +4,4 @@
 # the docs with readthedocs.org: they fetch a fresh version of the
 # library on each build and it doesn't import properly without tensorflow
 # being installed.
-tensorflow>=1.0
+tensorflow>=1.0,<=1.5

t3f/shapes_test.py

@@ -0,0 +1,16 @@
+import tensorflow as tf
+
+from t3f import initializers
+from t3f import shapes
+
+
+class ShapesTest(tf.test.TestCase):
+
+  def testLazyShapeOverflow(self):
+    large_shape = [10] * 20
+    tensor = initializers.random_matrix_batch([large_shape, large_shape], batch_size=5)
+    self.assertAllEqual([5, 10 ** 20, 10 ** 20], shapes.lazy_shape(tensor))
+
+
+if __name__ == "__main__":
+  tf.test.main()

t3f/tensor_train.py

@@ -8,21 +8,6 @@ class TensorTrain(TensorTrainBase):
   """Represents a Tensor Train object (a TT-tensor or TT-matrix).
 
   t3f represents a Tensor Train object as a tuple of TT-cores.
-  ```
-  @@__init__
-  @@get_raw_shape
-  @@get_shape
-  @@tt_cores
-  @@dtype
-  @@name
-  @@graph
-  @@ndims
-  @@get_tt_ranks
-  @@left_tt_rank_dim
-  @@right_tt_rank_dim
-  @@is_tt_matrix
-  @@is_variable
-  @@eval
   """
 
   def __init__(self, tt_cores, shape=None, tt_ranks=None, convert_to_tensors=True):

t3f/tensor_train_base.py

@@ -1,23 +1,11 @@
+from functools import reduce
 import numpy as np
 import tensorflow as tf
 
 
 # TODO: check the methods of _TensorLike
 class TensorTrainBase(object):
   """An abstract class that represents a collection of Tensor Train cores.
-  ```
-  @@__init__
-  @@get_raw_shape
-  @@get_shape
-  @@tt_cores
-  @@dtype
-  @@name
-  @@graph
-  @@ndims
-  @@get_tt_ranks
-  @@is_tt_matrix
-  @@is_variable
-  @@eval
   """
 
   def __init__(self, tt_cores):
@@ -43,9 +31,11 @@ def get_shape(self):
     """
     raw_shape = self.get_raw_shape()
     if self.is_tt_matrix():
+      # Use python prod instead of np.prod to avoid overflows.
+      prod_f = lambda arr: reduce(lambda x, y: x*y, arr)
       # TODO: as list is not available if shape is partly known.
-      m = np.prod(raw_shape[0].as_list())
-      n = np.prod(raw_shape[1].as_list())
+      m = prod_f(raw_shape[0].as_list())
+      n = prod_f(raw_shape[1].as_list())
       return tf.TensorShape((m, n))
     else:
       return raw_shape[0]

t3f/tensor_train_batch.py

@@ -10,21 +10,6 @@ class TensorTrainBatch(TensorTrainBase):
   """Represents a batch of Tensor Train objects (TT-tensors or TT-matrices).
 
   t3f represents a Tensor Train object as a tuple of TT-cores.
-  ```
-  @@__init__
-  @@get_raw_shape
-  @@get_shape
-  @@tt_cores
-  @@dtype
-  @@name
-  @@graph
-  @@ndims
-  @@get_tt_ranks
-  @@left_tt_rank_dim
-  @@right_tt_rank_dim
-  @@is_tt_matrix
-  @@is_variable
-  @@eval
   """
 
   def __init__(self, tt_cores, shape=None, tt_ranks=None, batch_size=None,

t3f/tensor_train_batch_test.py

@@ -72,6 +72,12 @@ def testPlaceholderTensorIndexing(self):
       desired, actual = sess.run([desired, actual], {start: 1, end: 3})
       self.assertAllClose(desired, actual)
 
+  def testShapeOverflow(self):
+    large_shape = [10] * 20
+    tensor = initializers.random_matrix_batch([large_shape, large_shape], batch_size=5)
+    shape = tensor.get_shape()
+    self.assertEqual([5, 10 ** 20, 10 ** 20], shape)
+
 
 if __name__ == "__main__":
   tf.test.main()

t3f/tensor_train_test.py

@@ -134,5 +134,11 @@ def testPlaceholderTensorIndexing(self):
       desired, actual = sess.run([desired, actual], {start: 1, end: 3})
       self.assertAllClose(desired, actual)
 
+  def testShapeOverflow(self):
+    large_shape = [10] * 20
+    matrix = initializers.matrix_zeros([large_shape, large_shape])
+    shape = matrix.get_shape()
+    self.assertEqual([10 ** 20, 10 ** 20], shape)
+
 if __name__ == "__main__":
   tf.test.main()

t3f/utils.py

@@ -81,3 +81,12 @@ def max_tt_ranks(raw_shape):
   right_to_left = robust_cumprod(raw_shape[::-1])[::-1]
   tt_ranks[1:-1] = np.minimum(left_to_right[:-1], right_to_left[1:])
   return tt_ranks
+
+
+def in_eager_mode():
+  """Checks whether tensorflow eager mode is avaialable and active."""
+  try:
+      from tensorflow.python.eager import context
+      return context.in_eager_mode()
+  except ImportError:
+      return False

t3f/variables.py

@@ -2,6 +2,7 @@
 
 from t3f.tensor_train import TensorTrain
 from t3f.tensor_train_batch import TensorTrainBatch
+from t3f import utils
 
 
 def get_variable(name,
@@ -55,7 +56,8 @@ def get_variable(name,
     raise ValueError('Scope reuse is False and initializer is not provided.')
 
   variable_cores = []
-  if reuse:
+
+  if reuse and not utils.in_eager_mode():
     # Find an existing variable in the collection.
     path = tf.get_variable_scope().name
     if path != '' and path[-1] != '/':