Python 3 support for SLING API

.travis.yml

@@ -3,7 +3,7 @@ language:
   - cpp
   - python
 compiler: gcc
-python: "2.7"
+python: "3.5"
 
 addons:
   apt:
@@ -13,10 +13,11 @@ addons:
       - wget
       - pkg-config
       - g++-4.8
+      - python3.5-dev
 
 before_install:
-  - wget https://github.com/bazelbuild/bazel/releases/download/0.8.0/bazel_0.8.0-linux-x86_64.deb
-  - sudo dpkg -i bazel_0.8.0-linux-x86_64.deb
+  - wget https://github.com/bazelbuild/bazel/releases/download/0.13.0/bazel_0.13.0-linux-x86_64.deb
+  - sudo dpkg -i bazel_0.13.0-linux-x86_64.deb
 
 script:
   - tools/buildall.sh

doc/guide/install.md

@@ -4,10 +4,10 @@
 
 If you just want to try out the parser on a pre-trained model, you can install
 the wheel with pip and download a pre-trained parser model. On a Linux machine
-with Python 2.7 you can install a pre-built wheel:
+with Python 3.5 you can install a pre-built wheel:
 
 ```
-sudo pip install http://www.jbox.dk/sling/sling-2.0.0-cp27-none-linux_x86_64.whl
+sudo pip3 install http://www.jbox.dk/sling/sling-2.0.0-cp35-none-linux_x86_64.whl
 ```
 and download the pre-trained model:
 ```
@@ -38,28 +38,30 @@ git clone https://github.com/google/sling.git
 cd sling
 ```
 
-SLING uses [Bazel](https://bazel.build/) as the build system, so you need to
-[install Bazel](https://docs.bazel.build/versions/master/install.html) in order
-to build the SLING parser.
-
+Next, run the `seup.sh` script to set up the SLING development environment
+and build the code:
 ```shell
-sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python2.7 python2.7-dev
-wget -P /tmp https://github.com/bazelbuild/bazel/releases/download/0.13.0/bazel-0.13.0-installer-linux-x86_64.sh
-chmod +x /tmp/bazel-0.13.0-installer-linux-x86_64.sh
-sudo /tmp/bazel-0.13.0-installer-linux-x86_64.sh
+./setup.sh
 ```
 
-The parser trainer uses Python v2.7 and PyTorch for training, so they need to be
-installed.
+This will perform the following steps:
+* Install missing package dependencies, notably GCC and Python 3.
+* Install [Bazel](https://bazel.build/) which is used as the build system for
+  SLING.
+* Build SLING from source.
+* Remove the Python 2.7 SLING pip package if it is installed.
+* Set up link to the SLING development enviroment for SLING Python 3 API.
+
+The parser trainer uses PyTorch for training, so it also needs to be installed:
 
 ```shell
-sudo pip install http://download.pytorch.org/whl/cpu/torch-0.3.1-cp27-cp27mu-linux_x86_64.whl
+sudo pip3 install http://download.pytorch.org/whl/cpu/torch-0.3.1-cp35-cp35mu-linux_x86_64.whl
 ```
 
 ## Building
 
 Operating system: Linux<br>
-Languages: C++, Python 2.7, assembler<br>
+Languages: C++ (gcc or clang), Python 3.5+, assembler<br>
 CPU: Intel x64 or compatible<br>
 Build system: Bazel<br>
 
@@ -69,11 +71,12 @@ You can use the `buildall.sh` script to build all the source code:
 tools/buildall.sh
 ```
 
-You then need to link the sling Python module directly to the Python source
-directory to use it in "developer mode":
+If you haven't run the `setup.sh` script already, you then need to link the
+sling Python module directly to the Python source directory to use it in
+"developer mode":
 
 ```shell
-sudo ln -s $(realpath python) /usr/lib/python2.7/dist-packages/sling
+sudo ln -s $(realpath python) /usr/lib/python3/dist-packages/sling
 ```
 
 **NOTE:**

doc/guide/myelin.md

@@ -152,15 +152,15 @@ data = cell.instance()
 
 # Set input.
 xdata = data[x]
-for i in xrange(64): xdata[0, i] = 5
+for i in range(64): xdata[0, i] = 5
 
 # Run computation for data instance.
 data.compute()
 
 # Print result.
 ydata = data[y]
-print "y", ydata
-print "argmax", np.asarray(ydata).argmax()
+print("y", ydata)
+print("argmax", np.asarray(ydata).argmax())
 ```
 
 The index operator on the cell object (e.g. `data[x]`) returns a _tensor_ object
@@ -217,15 +217,15 @@ data = cell.instance()
 
 # Set input.
 xdata = data[x]
-for i in xrange(64): xdata[0, i] = 5
+for i in range(64): xdata[0, i] = 5
 
 # Run computation for data instance.
 data.compute()
 
 # Print result.
 ydata = data[y]
-print "y", ydata
-print "argmax", np.asarray(ydata).argmax()
+print("y", ydata)
+print("argmax", np.asarray(ydata).argmax())
 ```
 
 ## Creating a flow file from a Tensorflow graph

doc/guide/pyapi.md

@@ -3,14 +3,9 @@
 A number of components in SLING can be accessed through the Python SLING API.
 You can install the SLING Python wheel using pip:
 ```
-sudo pip install http://www.jbox.dk/sling/sling-2.0.0-cp27-none-linux_x86_64.whl
-```
-or you can [clone the repo and build SLING from sources](install.md). You can
-then link the `sling` Python module directly to the Python source directory to
-use it in "developer mode":
-```
-sudo ln -s $(realpath python) /usr/lib/python2.7/dist-packages/sling
+sudo pip3 install http://www.jbox.dk/sling/sling-2.0.0-cp35-none-linux_x86_64.whl
 ```
+or you can [clone the repo and build SLING from sources](install.md).
 
 # Table of contents
 
@@ -61,25 +56,25 @@ doc = store['document']
 ```
 Role values for frames can be accessed as attributes:
 ```
-print doc.name
+print(doc.name)
 ```
 or using indexing:
 ```
-print doc['name']
+print(doc['name'])
 ```
 You can also use a frame value to access roles:
 ```
-print doc[name]
+print(doc[name])
 ```
 You can test if a frame has a role:
 ```
-if 'name' in doc: print "doc has 'name'"
-if name in doc: print "doc has name"
+if 'name' in doc: print("doc has 'name'")
+if name in doc: print("doc has name")
 ```
 You can iterate over all the named frames (i.e. frames with an `id:` slot)
 in a store:
 ```
-for f in store: print f.id
+for f in store: print(f.id)
 ```
 The `parse()` method can be used for adding new frames to the store:
 ```
@@ -121,24 +116,24 @@ f.extend([('foo', 10), ('bar': 20)])
 All the slots in a frame can be iterated:
 ```
 for name, value in f:
-  print "slot", name,"=", value
+  print("slot", name,"=", value)
 ```
 or just the roles with a particular name:
 ```
 for r in doc('role'):
-  print "doc role", r
+  print("doc role", r)
 ```
 Frames can be encoded in text format with the `data()` method:
 ```
-print f.data()
+print(f.data())
 ```
 and with indentation:
 ```
-print f.data(pretty=True)
+print(f.data(pretty=True))
 ```
 or with binary encoding:
 ```
-print len(f.data(binary=True))
+print(len(f.data(binary=True)))
 ```
 Arrays can be created with the `array()` method:
 ```
@@ -154,9 +149,9 @@ a[2] = 3
 SLING arrays work much in the same way as Python lists except that they have
 a fixed size:
 ```
-print len(a)
-print a[1]
-for item in a: print item
+print(len(a))
+print(a[1])
+for item in a: print(item)
 ```
 Finally, a store can be save to a file in textual encoding:
 ```
@@ -181,7 +176,7 @@ import sling
 
 recin = sling.RecordReader("test.rec")
 for key,value in recin:
-  print key, value
+  print(key, value)
 recin.close()
 ```
 The `RecordReader` class has the following methods:
@@ -236,7 +231,7 @@ writer.close()
 # Look up each record in record database.
 db = sling.RecordDatabase("/tmp/test.rec")
 for i in range(N):
-  print db.lookup(str(i))
+  print(db.lookup(str(i)))
 db.close()
 ```
 
@@ -310,7 +305,7 @@ for _,rec in corpus:
   num_docs += 1
   num_tokens += len(doc.tokens)
 
-print "docs:", num_docs, "tokens:", num_tokens
+print("docs:", num_docs, "tokens:", num_tokens)
 ```
 
 Example: read text from a file and create a corpus of tokenized documents:
@@ -464,7 +459,7 @@ The `Corpus` class can be used for iterating over a corpus of documents stored i
 record files:
 ```
 for document in sling.Corpus("local/data/e/wiki/en/documents@10.rec"):
-  print document.text
+  print(document.text)
 ```
 This will create a global store with the document schema symbols and create
 a local store for each document. If you have a global store you can use this
@@ -474,7 +469,7 @@ kb = sling.Store()
 corpus = sling.Corpus("local/data/e/wiki/en/documents@10.rec", commons=kb)
 kb.freeze()
 for document in corpus:
-  print document.text
+  print(document.text)
 ```
 ### LEX format
 
@@ -560,11 +555,11 @@ kb.freeze()
 
 # Lookup entities with name 'Annette Stroyberg'.
 for entity in names.lookup("Annette Stroyberg"):
-  print entity.id, entity.name
+  print(entity.id, entity.name)
 
 # Query all entities named 'Funen' with frequency counts.
 for m in names.query("Funen"):
-  print m.count(), m.id(), m.item().name, "(", m.item().description, ")"
+  print(m.count(), m.id(), m.item().name, "(", m.item().description, ")")
 ```
 
 The `lookup()` and `query()` methods return the matches in decreasing
@@ -593,7 +588,7 @@ for Annette Stroyberg ([Q2534120](https://www.wikidata.org/wiki/Q2534120)):
 ```
 entity = kb["Q2534120"]
 dob = sling.Date(entity["P569"])
-print dob.year, dob.month, dob.day
+print(dob.year, dob.month, dob.day)
 ```
 
 The `Date` class has the following properties and methods:
@@ -688,7 +683,7 @@ The `flags.define()` function takes the same arguments as the standard Python
 method. You can then access the flags as variables in the flags module, e.g.:
 ```
   if flags.verbose:
-    print "verbose output..."
+    print("verbose output...")
 ```
 
 The flags parser must be initialized in the main method of your Python program:
@@ -712,5 +707,5 @@ url = "https://www.wikidata.org/wiki/Special:EntityData/" + qid + ".json"
 json = urllib2.urlopen(url).read()[len(qid) + 16:-2]
 
 item = wikiconv.convert_wikidata(store, json)
-print item.data(pretty=True)
+print(item.data(pretty=True))
 ```

doc/guide/training.md

@@ -195,7 +195,7 @@ the same script to create the commons store behind the scenes. But we mention
 this here in case one wishes to inspect the automatically created commons.
 
 ```shell
-python sling/nlp/parser/tools/commons_from_corpora.py \
+python3 sling/nlp/parser/tools/commons_from_corpora.py \
   --input=<path to train.rec>,<path to dev.rec>,<any other rec files> \
   --output=<path where commons will be written>
 ```

python/__init__.py

@@ -1,4 +1,5 @@
 import sling.pysling as api
+
 from sling.log import *
 from sling.nlp.document import *
 from sling.nlp.parser import *

python/flags.py

@@ -15,7 +15,7 @@
 """Command-line flags"""
 
 import argparse
-import pysling as api
+import sling.pysling as api
 
 # Command line flag arguments.
 arg = argparse.Namespace()

python/log.py

@@ -16,7 +16,7 @@
 
 import inspect
 import os
-import pysling as api
+import sling.pysling as api
 
 INFO = 0
 WARNING = 1

python/myelin/__init__.py

@@ -1,7 +1,7 @@
 import sling.pysling as api
 
-from builder import *
-from flow import *
+from .builder import *
+from .flow import *
 
 Compiler=api.Compiler
 

python/myelin/builder.py

@@ -15,10 +15,7 @@
 
 """Myelin function builder and expression evaluator."""
 
-import flow
-from flow import Variable
-from flow import Function
-from flow import Flow
+from .flow import set_builder_factory, Variable
 
 DT_FLOAT32 = "float32"
 DT_FLOAT64 = "float64"
@@ -160,7 +157,7 @@ def split(self, x, splits, axis=0, name=None):
     shape = x.shape[:]
     shape[axis] = x.shape[axis] / splits
     results = []
-    for n in xrange(splits):
+    for n in range(splits):
       o = self.var(op.name + ":" + str(n), x.type, shape)
       op.add_output(o)
       results.append(o)
@@ -379,5 +376,5 @@ def rank(self, x, name=None):
 def builder_factory(flow, name):
   return Builder(flow, name)
 
-flow.builder_factory = builder_factory
+set_builder_factory(builder_factory)