Merge pull request #2322 from lamblin/travis_py3

.travis.yml

@@ -4,8 +4,6 @@
 language: python
 python:
   - "2.6"
-#  - "2.7"
-#  - "3.2"
 
 # command to install dependencies
 before_install:
@@ -20,25 +18,38 @@ before_install:
 install:
 # We support scipy 0.7.2, but it is not available on conda.
 # So we test with 0.11. Our internal buildbot have 0.7.2.
-  - conda create --yes -q -n py26 python=2.6 numpy=1.6 scipy=0.11 nose=1.1 pip
-  - source activate py26
-  - pip install pydot
+  - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then conda create --yes -q -n pyenv python=2.6 numpy=1.6 scipy=0.11 nose=1.1 pyparsing=1.5 pip; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == '3.3' ]]; then conda create --yes -q -n pyenv python=3.3 numpy=1.9.1 scipy=0.14.0 nose=1.3.4 pip; fi
+  - source activate pyenv
+  - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then pip install pydot; fi
   - pip install . --no-deps --use-mirrors
 
 # command to run tests
 env:
-  - PART="theano/scan_module/"
-  - PART="theano/sandbox theano/sparse theano/scalar/ theano/tensor/nnet/"
-  - PART="theano/tensor/tests/test_basic.py theano/tensor/signal/ theano/compile/ theano/gof/ theano/misc/ theano/tests/ theano/compat"
-  - PART="-e test_basic.py theano/tensor/tests"
+  - PART="scan_module"
+  - PART="sandbox sparse scalar tensor/nnet"
+  - PART="tensor/tests/test_basic.py tensor/signal compile gof misc tests compat"
+  - PART="-e test_basic.py tensor/tests"
+
+matrix:
+  # Explicitly add one part to be run with Python 3, and a complete run in FAST_COMPILE
+  include:
+    - python: "3.3"
+      env: PART="tensor/tests/test_basic.py tensor/signal compile gof misc tests compat"
+    - python: "2.6"
+      env: PART="." THEANO_FLAGS="mode=FAST_COMPILE"
 
 script:
-  - export THEANO_FLAGS=blas.ldflags="-lblas -lgfortran",warn.ignore_bug_before=all,on_opt_error=raise,on_shape_error=raise
+  - export THEANO_FLAGS=$THEANO_FLAGS,blas.ldflags="-lblas -lgfortran",warn.ignore_bug_before=all,on_opt_error=raise,on_shape_error=raise
   - python --version
   - uname -a
   - free -m
   - df -h
   - ulimit -a
+  # Move out of Theano so the import will use the installed version
+  - cd ..
+  # Move to the path of the installed version
+  - cd $(python -c 'import theano; import os; print(os.path.split(theano.__file__)[0])')
   - echo "$PART"
   - theano-nose -v $PART
   - theano-cache list

theano/sandbox/cuda/tests/test_mlp.py

@@ -275,8 +275,8 @@ def run_conv_nnet2(use_gpu):  # pretend we are training LeNet for MNIST
     lr = tensor.fscalar('lr')
 
     conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern, n_batch, 1, 1)
-    conv_op1 = conv.ConvOp((n_kern, logical_hid_shape[0] / 2,
-         logical_hid_shape[1] / 2), shape_kern1[2:], n_kern1, n_batch, 1, 1)
+    conv_op1 = conv.ConvOp((n_kern, logical_hid_shape[0] // 2,
+         logical_hid_shape[1] // 2), shape_kern1[2:], n_kern1, n_batch, 1, 1)
 
     hid = tensor.tanh(conv_op(x, w0) + b0.dimshuffle((0, 'x', 'x')))
     hid1 = tensor.tanh(conv_op1(hid[:, :, ::2, ::2], w1) + b1.dimshuffle((
@@ -366,7 +366,7 @@ def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
     conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern,
                           n_batch, 1, 1, verbose=verbose, version=version)
     conv_op1 = conv.ConvOp(
-        (n_kern, logical_hid_shape[0] / 2, logical_hid_shape[1] / 2),
+        (n_kern, logical_hid_shape[0] // 2, logical_hid_shape[1] // 2),
         shape_kern1[2:], n_kern1, n_batch, 1, 1,verbose=verbose, version=version)
 
     ds_op = downsample.DownsampleFactorMax((2, 2), ignore_border=False)

theano/sandbox/linalg/tests/test_linalg.py

@@ -147,11 +147,12 @@ def test_transinv_to_invtrans():
     Y = tensor.nlinalg.matrix_inverse(X)
     Z = Y.transpose()
     f = theano.function([X], Z)
-    for node in f.maker.fgraph.toposort():
-        if isinstance(node.op, MatrixInverse):
-            assert isinstance(node.inputs[0].owner.op, DimShuffle)
-        if isinstance(node.op, DimShuffle):
-            assert node.inputs[0].name == 'X'
+    if config.mode != 'FAST_COMPILE':
+        for node in f.maker.fgraph.toposort():
+            if isinstance(node.op, MatrixInverse):
+                assert isinstance(node.inputs[0].owner.op, DimShuffle)
+            if isinstance(node.op, DimShuffle):
+                assert node.inputs[0].name == 'X'
 
 
 def test_tag_solve_triangular():
@@ -164,13 +165,12 @@ def test_tag_solve_triangular():
     b1 = solve(L, x)
     b2 = solve(U, x)
     f = theano.function([A,x], b1)
-    for node in f.maker.fgraph.toposort():
-        if isinstance(node.op, Solve):
-            assert node.op.A_structure == 'lower_triangular'
+    if config.mode != 'FAST_COMPILE':
+        for node in f.maker.fgraph.toposort():
+            if isinstance(node.op, Solve):
+                assert node.op.A_structure == 'lower_triangular'
     f = theano.function([A,x], b2)
-    for node in f.maker.fgraph.toposort():
-        if isinstance(node.op, Solve):
-            assert node.op.A_structure == 'upper_triangular'
-
-
-        
+    if config.mode != 'FAST_COMPILE':
+        for node in f.maker.fgraph.toposort():
+            if isinstance(node.op, Solve):
+                assert node.op.A_structure == 'upper_triangular'

theano/scan_module/scan_opt.py

@@ -470,10 +470,10 @@ class PushOutSeqScan(gof.Optimizer):
                         outside_ins = replace_with_out[to_replace.index(x)]
                     new_ord = (0,)
                     for old_ord in nd.op.new_order:
-                        if isinstance(old_ord, int):
-                            new_ord += (old_ord + 1,)
-                        else:
+                        if (old_ord == 'x'):
                             new_ord += (old_ord,)
+                        else:
+                            new_ord += (old_ord + 1,)
                     new_outer = outside_ins.dimshuffle(new_ord)
                     y = nd.outputs[0]
                     y_place_holder = scan_utils.safe_new(y, '_replace')

theano/scan_module/tests/test_scan_opt.py

@@ -202,11 +202,11 @@ class TestPushOutScanOutputDot(object):
 
         # Compile the function twice, once with the optimization and once
         # without
-        f_opt = theano.function([a, b], outputs)
+        opt_mode = mode.including("scan")
+        f_opt = theano.function([a, b], outputs, mode=opt_mode)
 
-        default_mode = theano.compile.get_default_mode()
-        default_mode.excluding("scanOp_pushout_output")
-        f_no_opt = theano.function([a, b], outputs, mode=default_mode)
+        no_opt_mode = mode.excluding("scanOp_pushout_output")
+        f_no_opt = theano.function([a, b], outputs, mode=no_opt_mode)
 
         # Ensure that the optimization was performed correctly in f_opt
         # The inner function of scan should have only one output and it should