Merge pull request #13 from rsepassi/push

v1.0.5

.gitignore

@@ -0,0 +1,5 @@
+# Compiled python modules.
+*.pyc
+
+# Python egg metadata, regenerated from source files by setuptools.
+/*.egg-info

README.md

@@ -6,6 +6,7 @@ version](https://badge.fury.io/py/tensor2tensor.svg)](https://badge.fury.io/py/t
 Issues](https://img.shields.io/github/issues/tensorflow/tensor2tensor.svg)](https://github.com/tensorflow/tensor2tensor/issues)
 [![Contributions
 welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CONTRIBUTING.md)
+[![Gitter](https://img.shields.io/gitter/room/nwjs/nw.js.svg)](https://gitter.im/tensor2tensor/Lobby)
 [![License](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)](https://opensource.org/licenses/Apache-2.0)
 
 [T2T](https://github.com/tensorflow/tensor2tensor) is a modular and extensible
@@ -22,6 +23,8 @@ send along a pull request to add your data-set or model.
 See [our contribution
 doc](CONTRIBUTING.md) for details and our [open
 issues](https://github.com/tensorflow/tensor2tensor/issues).
+And chat with us and other users on
+[Gitter](https://gitter.im/tensor2tensor/Lobby).
 
 ---
 
@@ -95,7 +98,14 @@ cat $DECODE_FILE.$MODEL.$HPARAMS.beam$BEAM_SIZE.alpha$ALPHA.decodes
 ## Installation
 
 ```
+# Assumes tensorflow or tensorflow-gpu installed
 pip install tensor2tensor
+
+# Installs with tensorflow-gpu requirement
+pip install tensor2tensor[tensorflow_gpu]
+
+# Installs with tensorflow (cpu) requirement
+pip install tensor2tensor[tensorflow]
 ```
 
 Binaries:
@@ -191,6 +201,44 @@ related flags control local and distributed training/evaluation
 
 ---
 
+## Adding your own components
+
+T2T's components are registered using a central registration mechanism that
+enables easily adding new ones and easily swapping amongst them by command-line
+flag. You can add your own components without editing the T2T codebase by
+specifying the `--t2t_usr_dir` flag in `t2t-trainer`.
+
+You can currently do so for models, hyperparameter sets, and modalities. Please
+do submit a pull request if your component might be useful to others.
+
+Here's an example with a new hyperparameter set:
+
+```python
+# In ~/usr/t2t_usr/my_registrations.py
+
+from tensor2tensor.models import transformer
+from tensor2tensor.utils import registry
+
+@registry.register_hparams
+def transformer_my_very_own_hparams_set():
+  hparams = transformer.transformer_base()
+  hparams.hidden_size = 1024
+  ...
+```
+
+```python
+# In ~/usr/t2t_usr/__init__.py
+import my_registrations
+```
+
+```
+t2t-trainer --t2t_usr_dir=~/usr/t2t_usr --registry_help
+```
+
+You'll see under the registered HParams your
+`transformer_my_very_own_hparams_set`, which you can directly use on the command
+line with the `--hparams_set` flag.
+
 ## Adding a dataset
 
 See the [data generators

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.0.4',
+    version='1.0.5',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='[email protected]',
@@ -17,8 +17,11 @@
         'numpy',
         'sympy',
         'six',
-        'tensorflow-gpu>=1.2.0rc1',
     ],
+    extras_require={
+        'tensorflow': ['tensorflow>=1.2.0rc1'],
+        'tensorflow_gpu': ['tensorflow-gpu>=1.2.0rc1'],
+    },
     classifiers=[
         'Development Status :: 4 - Beta',
         'Intended Audience :: Developers',

tensor2tensor/bin/t2t-trainer

@@ -29,17 +29,45 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import importlib
+import os
+import sys
+
 # Dependency imports
 
 from tensor2tensor.utils import trainer_utils as utils
 
 import tensorflow as tf
 
-FLAGS = tf.flags.FLAGS
+flags = tf.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string("t2t_usr_dir", "",
+                    "Path to a Python module that will be imported. The "
+                    "__init__.py file should include the necessary imports. "
+                    "The imported files should contain registrations, "
+                    "e.g. @registry.register_model calls, that will then be "
+                    "available to the t2t-trainer.")
+
+
+def import_usr_dir():
+  """Import module at FLAGS.t2t_usr_dir, if provided."""
+  if not FLAGS.t2t_usr_dir:
+    return
+  dir_path = os.path.expanduser(FLAGS.t2t_usr_dir)
+  if dir_path[-1] == "/":
+    dir_path = dir_path[:-1]
+  containing_dir, module_name = os.path.split(dir_path)
+  tf.logging.info("Importing user module %s from path %s", module_name,
+                  containing_dir)
+  sys.path.insert(0, containing_dir)
+  importlib.import_module(module_name)
+  sys.path.pop(0)
 
 
 def main(_):
   tf.logging.set_verbosity(tf.logging.INFO)
+  import_usr_dir()
   utils.log_registry()
   utils.validate_flags()
   utils.run(

tensor2tensor/data_generators/lm_example.py

@@ -38,7 +38,7 @@
 tar xvf 1-billion-word-language-modeling-benchmark-r13output.tar.gz
 
 # replace oov words with UNK
-./blaze-bin/third_party/py/tensor2tensor/data_generators/replace_oov \
+$BINARYDIR/replace_oov \
 --vocab_file=$DATADIR/vocab-2016-09-10.txt \
 --in_filepattern=\
 $DATADIR/1-billion-word-language-modeling-benchmark-r13output/\

tensor2tensor/models/common_attention.py

@@ -271,10 +271,14 @@ def attention_image_summary(attn, image_shapes=None):
 
   Args:
     attn: a Tensor with shape [batch, num_heads, query_length, memory_length]
-    image_shapes: optional quadruple of integer scalars.
+    image_shapes: optional tuple of integer scalars.
       If the query positions and memory positions represent the
-      pixels of a flattened image, then pass in their dimensions:
+      pixels of flattened images, then pass in their dimensions:
         (query_rows, query_cols, memory_rows, memory_cols).
+      If the query positions and memory positions represent the
+      pixels x channels of flattened images, then pass in their dimensions:
+        (query_rows, query_cols, query_channels,
+         memory_rows, memory_cols, memory_channels).
   """
   num_heads = attn.get_shape().as_list()[1]
   # [batch, query_length, memory_length, num_heads]
@@ -286,10 +290,20 @@ def attention_image_summary(attn, image_shapes=None):
   image = split_last_dimension(image, 3)
   image = tf.reduce_max(image, 4)
   if image_shapes is not None:
-    q_rows, q_cols, m_rows, m_cols = list(image_shapes)
-    image = tf.reshape(image, [-1, q_rows, q_cols, m_rows, m_cols, 3])
-    image = tf.transpose(image, [0, 1, 3, 2, 4, 5])
-    image = tf.reshape(image, [-1, q_rows * m_rows, q_cols * m_cols, 3])
+    if len(image_shapes) == 4:
+      q_rows, q_cols, m_rows, m_cols = list(image_shapes)
+      image = tf.reshape(image, [-1, q_rows, q_cols, m_rows, m_cols, 3])
+      image = tf.transpose(image, [0, 1, 3, 2, 4, 5])
+      image = tf.reshape(image, [-1, q_rows * m_rows, q_cols * m_cols, 3])
+    else:
+      assert len(image_shapes) == 6
+      q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels = list(
+          image_shapes)
+      image = tf.reshape(image, [-1, q_rows, q_cols, q_channnels,
+                                 m_rows, m_cols, m_channels, 3])
+      image = tf.transpose(image, [0, 1, 4, 3, 2, 5, 6, 7])
+      image = tf.reshape(image, [-1, q_rows * m_rows * q_channnels,
+                                 q_cols * m_cols * m_channels, 3])
   tf.summary.image("attention", image, max_outputs=1)
 
 
@@ -310,10 +324,8 @@ def dot_product_attention(q,
     bias: bias Tensor (see attention_bias())
     dropout_rate: a floating point number
     summaries: a boolean
-    image_shapes: optional quadruple of integer scalars for image summary.
-      If the query positions and memory positions represent the
-      pixels of a flattened image, then pass in their dimensions:
-        (query_rows, query_cols, memory_rows, memory_cols).
+    image_shapes: optional tuple of integer scalars.
+      see comments for attention_image_summary()
     name: an optional string
 
   Returns:
@@ -356,10 +368,8 @@ def multihead_attention(query_antecedent,
     num_heads: an integer dividing total_key_depth and total_value_depth
     dropout_rate: a floating point number
     summaries: a boolean
-    image_shapes: optional quadruple of integer scalars for image summary.
-      If the query positions and memory positions represent the
-      pixels of a flattened image, then pass in their dimensions:
-        (query_rows, query_cols, memory_rows, memory_cols).
+    image_shapes: optional tuple of integer scalars.
+      see comments for attention_image_summary()
     name: an optional string
 
   Returns:
@@ -398,3 +408,72 @@ def multihead_attention(query_antecedent,
     x = combine_heads(x)
     x = common_layers.conv1d(x, output_depth, 1, name="output_transform")
     return x
+
+
+def parameter_attention(x,
+                        total_key_depth,
+                        total_value_depth,
+                        output_depth,
+                        memory_rows,
+                        num_heads,
+                        dropout_rate,
+                        name=None):
+  """Attention over parameters.
+
+  We use the same multi-headed attention as in the other layers, but the memory
+  keys and values are model parameters.  There are no linear transformation
+  on the keys or values.
+
+  We are also a bit more careful about memory usage, since the number of
+  memory positions may be very large.
+
+  Args:
+    x: a Tensor with shape [batch, length_q, channels]
+    total_key_depth: an integer
+    total_value_depth: an integer
+    output_depth: an integer
+    memory_rows: an integer
+    num_heads: an integer dividing total_key_depth and total_value_depth
+    dropout_rate: a floating point number
+    name: an optional string
+
+  Returns:
+    A Tensor.
+  """
+  with tf.variable_scope(name, default_name="parameter_attention",
+                         values=[x]):
+    head_size_k = total_key_depth // num_heads
+    head_size_v = total_value_depth // num_heads
+    var_shape_k = [num_heads, memory_rows, head_size_k]
+    var_shape_v = [num_heads, memory_rows, head_size_v]
+    k = tf.get_variable(
+        "k", var_shape_k,
+        initializer=tf.random_normal_initializer(
+            0, output_depth ** -0.5)) * (num_heads ** 0.5)
+    v = tf.get_variable(
+        "v", var_shape_v,
+        initializer=tf.random_normal_initializer(
+            0, output_depth ** -0.5)) * (output_depth ** 0.5)
+    batch_size = tf.shape(x)[0]
+    length = tf.shape(x)[1]
+    q = common_layers.conv1d(x, total_key_depth, 1, name="q_transform")
+    if dropout_rate:
+      # This is a cheaper form of attention dropout where we use to use
+      # the same dropout decisions across batch elemets and query positions,
+      # but different decisions across heads and memory positions.
+      v = tf.nn.dropout(v, 1.0 - dropout_rate,
+                        noise_shape=[num_heads, memory_rows, 1])
+    # query is [batch, length, hidden_size]
+    # reshape and transpose it to [heads, batch * length, head_size]
+    q = tf.reshape(q, [batch_size, length, num_heads, head_size_k])
+    q = tf.transpose(q, [2, 0, 1, 3])
+    q = tf.reshape(q, [num_heads, batch_size * length, head_size_k])
+    weights = tf.matmul(q, k, transpose_b=True)
+    weights = tf.nn.softmax(weights)
+    y = tf.matmul(weights, v)
+    y = tf.reshape(y, [num_heads, batch_size, length, head_size_v])
+    y = tf.transpose(y, [1, 2, 0, 3])
+    y = tf.reshape(y, [batch_size, length, total_value_depth])
+    y.set_shape([None, None, total_value_depth])
+    y = common_layers.conv1d(y, output_depth, 1, name="output_transform")
+    return y

tensor2tensor/models/modalities.py

@@ -441,8 +441,8 @@ class IdentityModality(modality.Modality):
   def targets_dimensionality(self):
     return self._vocab_size
 
-  def inputs_bottom_simple(self, inputs):
-    return tf.to_float(inputs)
+  def bottom(self, x):
+    return tf.to_float(x)
 
-  def targets_top_simple(self, body_output, _):
+  def top(self, body_output, _):
     return body_output