* mode.py now defines default_mode, which is read from THEANO_DEFAULT_MODE env.…

theano/__init__.py

@@ -57,6 +57,7 @@ import tensor
 import scalar
 import sparse
 import gradient
+import gof
 
 
 ## import scalar_opt

theano/compile/__init__.py

@@ -14,5 +14,7 @@ from builders import *
 import module
 from module import *
 
+import debugmode
+
 from profilemode import ProfileMode
 

theano/compile/debugmode.py

@@ -11,13 +11,9 @@ from ..gof import Env, graph, utils, link
 from ..gof.link import WrapLinkerMany, raise_with_op
 from ..gof.cutils import run_cthunk
 from ..gof.cc import OpWiseCLinker, CLinker
-from ..compile.mode import Mode
 import numpy
 
-
-from ..compile.function_module import (convert_function_input, 
-        FunctionMaker,
-        predefined_modes,
+from ..compile.function_module import (FunctionMaker,
         Function, 
         infer_reuse_pattern,
         SymbolicInput,
@@ -622,6 +618,9 @@ class DebugModeFunctionMaker(FunctionMaker): #inheritance buys a few helper func
         fn = self.function_builder(_fn, _i, _o, self.indices, self.outputs, defaults, self.unpack_single, self)
         return fn
 
+
+
+from ..compile.mode import Mode, register_mode
 class DebugMode(Mode):
     """Evaluation Mode that detects optimization errors.
 
@@ -648,6 +647,4 @@ class DebugMode(Mode):
                 linker=DebugModeLinker)
         self.stability_patience = stability_patience
         self.check_c_code = check_c_code
-
-
-
+register_mode('DEBUG_MODE',DebugMode(optimizer='fast_run'))

theano/compile/function_module.py

@@ -15,10 +15,6 @@ from copy import copy
 from mode import *
 from io import *
 
-
-# used by function and module as the default compilation mode
-mode_default = 'FAST_COMPILE'
-
 def infer_reuse_pattern(env, outputs_to_disown):
     """
     Given an env and a list of results, returns the list of all
@@ -454,7 +450,7 @@ class FunctionMaker(object):
             raise TypeError("Unknown output type: %s (%s)", type(output), output)
 
     def __init__(self, inputs, outputs, 
-            mode = mode_default, accept_inplace = False, function_builder = Function):
+            mode = default_mode, accept_inplace = False, function_builder = Function):
         """
         :type inputs: a list of SymbolicInput instances
 
@@ -644,7 +640,7 @@ def register_checker(checker):
 
 
 
-def function(inputs, outputs, mode=mode_default, accept_inplace = False):
+def function(inputs, outputs, mode=default_mode, accept_inplace = False):
     """
     Return a function calculating the outputs from the inputs.
 

theano/compile/mode.py

-
 import numpy
+import os
 import scipy.sparse as sp
 from .. import gof
 
@@ -144,16 +144,21 @@ class Mode(object):
 # If a string is passed as the mode argument in function or
 # FunctionMaker, the Mode will be taken from this dictionary using the
 # string as the key
-
 FAST_COMPILE = Mode('py', 'fast_compile')
 FAST_RUN = Mode('c|py', 'fast_run')
 SANITY_CHECK = [Mode('c|py', None),
                 Mode('c|py', 'fast_run')]
-
 predefined_modes = {'FAST_COMPILE': FAST_COMPILE,
                     'FAST_RUN': FAST_RUN,
                     'SANITY_CHECK': SANITY_CHECK}
-default_mode = 'FAST_COMPILE'
+
+
+##
+# The default mode used by functions and modules is read from the environment
+# variable THEANO_DEFAULT_MODE. Unit tests will run using this value. If the env. var.
+# is not set, it will default to 'FAST_RUN'
+##
+default_mode = os.getenv('THEANO_DEFAULT_MODE','FAST_RUN')
 
 def register_mode(name, mode):
     """Add a `Mode` which can be referred to by `name` in `function`."""

theano/compile/module.py

@@ -7,6 +7,7 @@ from functools import partial
 from copy import copy
 import io
 import function_module as F
+from mode import default_mode
 
 
 def join(*args):
@@ -125,7 +126,7 @@ class Component(object):
         """
         raise NotImplementedError
 
-    def make_no_init(self, mode=F.mode_default):
+    def make_no_init(self, mode=default_mode):
         """
         Allocates the necessary containers using allocate() and uses
         build() with the provided mode to make an instance which will
@@ -145,7 +146,7 @@ class Component(object):
         arguments and the keyword arguments. If 'mode' is in the
         keyword arguments it will be passed to build().
         """
-        mode = kwargs.pop('mode', F.mode_default)
+        mode = kwargs.pop('mode', default_mode)
         rval = self.make_no_init(mode)
         if hasattr(rval, 'initialize'):
             rval.initialize(*args, **kwargs)
@@ -958,7 +959,7 @@ class Module(ComponentDict):
         """
         self.make_mi(args,kwargs)
 
-        mode = kwargs.pop('mode', F.mode_default)
+        mode = kwargs.pop('mode', default_mode)
         rval = self.make_no_init(mode)
         if hasattr(rval, 'initialize'):
             rval.initialize(*args, **kwargs)
@@ -1011,10 +1012,3 @@ class KitComponent(Component):
 
     def build(self, mode, memo):
         return [memo[i.result].value for i in self.kit.sinputs]
-
-
-
-
-
-
-

theano/printing.py

@@ -37,6 +37,13 @@ class Print(Op):
     def grad(self,input,output_gradients):
         return output_gradients
 
+    def __eq__(self, other):
+        return type(self)==type(other) and self.message==other.message and self.attrs==other.attrs
+
+    def __hash__(self):
+        return hash(self.message) ^ hash(self.attrs)
+
+
 class PrinterState(gof.utils.scratchpad):
     
     def __init__(self, props = {}, **more_props):

theano/sparse/tests/test_basic.py

@@ -335,22 +335,21 @@ class test_structureddot(unittest.TestCase):
             return structured_dot(csc, images.T)
         out = buildgraphCSC(kerns,images)
 
-        for mode in 'FAST_COMPILE','FAST_RUN':
-            f = theano.function([kerns,images], out, mode=mode)
-            kernvals = spmat.data[:spmat.size]
-            imvals = 1.0 * numpy.arange(bsize*spmat.shape[1]).reshape(bsize,spmat.shape[1])
-            outvals = f(kernvals,imvals)
-            print type(spmat.dot(imvals.T))
-            print spmat.dot(imvals.T)
-            print dir(spmat.dot(imvals.T))
-
-#           scipy 0.7.0 should already make the output dense
-#            assert numpy.all(outvals == spmat.dot(imvals.T).todense())
-            c = spmat.dot(imvals.T)
-            assert _is_dense(c)
-            assert numpy.all(outvals == c)
-
-            tensor.verify_grad(None, buildgraphCSC, [kernvals,imvals], mode=mode)
+        f = theano.function([kerns,images], out)
+        kernvals = spmat.data[:spmat.size]
+        imvals = 1.0 * numpy.arange(bsize*spmat.shape[1]).reshape(bsize,spmat.shape[1])
+        outvals = f(kernvals,imvals)
+        print type(spmat.dot(imvals.T))
+        print spmat.dot(imvals.T)
+        print dir(spmat.dot(imvals.T))
+
+#       scipy 0.7.0 should already make the output dense
+#       assert numpy.all(outvals == spmat.dot(imvals.T).todense())
+        c = spmat.dot(imvals.T)
+        assert _is_dense(c)
+        assert numpy.all(outvals == c)
+
+        tensor.verify_grad(None, buildgraphCSC, [kernvals,imvals])
 
         spmat = spmat.tocsr()
         def buildgraphCSR(kerns,images):
@@ -358,19 +357,18 @@ class test_structureddot(unittest.TestCase):
             return structured_dot(csr, images.T)
         out = buildgraphCSR(kerns,images)
 
-        for mode in 'FAST_COMPILE','FAST_RUN':
-            f = theano.function([kerns,images], out, mode=mode)
-            kernvals = spmat.data[:spmat.size]
-            imvals = 1.0 * numpy.arange(bsize*spmat.shape[1]).reshape(bsize,spmat.shape[1])
-            outvals = f(kernvals,imvals)
+        f = theano.function([kerns,images], out)
+        kernvals = spmat.data[:spmat.size]
+        imvals = 1.0 * numpy.arange(bsize*spmat.shape[1]).reshape(bsize,spmat.shape[1])
+        outvals = f(kernvals,imvals)
 
-#           scipy 0.7.0 should already make the output dense
-#            assert numpy.all(outvals == spmat.dot(imvals.T).todense())
-            c = spmat.dot(imvals.T)
-            assert _is_dense(c)
-            assert numpy.all(outvals == c)
+#       scipy 0.7.0 should already make the output dense
+#       assert numpy.all(outvals == spmat.dot(imvals.T).todense())
+        c = spmat.dot(imvals.T)
+        assert _is_dense(c)
+        assert numpy.all(outvals == c)
 
-            tensor.verify_grad(None, buildgraphCSR, [kernvals,imvals], mode=mode)
+        tensor.verify_grad(None, buildgraphCSR, [kernvals,imvals])
 
 
 if __name__ == '__main__':

theano/tensor/tests/test_blas.py

@@ -11,7 +11,7 @@ _as_scalar = GemmLocalOptimizer._as_scalar
 _is_real_matrix = GemmLocalOptimizer._is_real_matrix
 
 from theano import In, Out
-from .test_basic import (_approx_eq, as_tensor, function,
+from .test_basic import (_approx_eq, as_tensor, inplace_func,
         compile, value, constant, inplace, eval_outputs)
 
 class t_gemm(TestCase):
@@ -36,7 +36,7 @@ class t_gemm(TestCase):
             z_orig = z.copy()
             tz,ta,tx,ty,tb = [as_tensor(p).type() for p in z,a,x,y,b]
 
-            f = function([tz,ta,tx,ty,tb], gemm(tz,ta,tx,ty,tb), mode=compile.Mode(optimizer = None, linker = l))
+            f = inplace_func([tz,ta,tx,ty,tb], gemm(tz,ta,tx,ty,tb), mode=compile.Mode(optimizer = None, linker = l))
             new_z = f(z,a,x,y,b)
             z_after = self._gemm(z_orig, a, x, y, b)
 
@@ -158,7 +158,7 @@ class t_gemm(TestCase):
 
             tz,ta,tx,ty,tb = [value(p) for p in z,a,x,y,b]
 
-            f = function([tz,ta,tx,ty,tb], gemm(tz,ta,tx,ty,tb), mode = compile.Mode(optimizer = None, linker=l))
+            f = inplace_func([tz,ta,tx,ty,tb], gemm(tz,ta,tx,ty,tb), mode = compile.Mode(optimizer = None, linker=l))
             f(z, a, x, y, b)
             self.failUnless(_approx_eq(z_after, z), (z_orig, z_after, z, z_after - z))
 
@@ -256,11 +256,11 @@ class Warning(Exception):
 
 def just_gemm(i, o, ishapes = [(4,3), (3,5), (4,5), (), ()]):
     try:
-        f = function([In(ii, mutable=True) for ii in i],o, mode='FAST_RUN')
+        f = inplace_func([In(ii, mutable=True) for ii in i],o, mode='FAST_RUN')
         for node in f.maker.env.nodes:
             if node.op == T.dot: raise Warning('dot not changed to gemm in graph')
             if node.op == _dot22: raise Warning('_dot22 not changed to gemm in graph')
-        g = function(i, o, mode=compile.Mode(linker='py', optimizer=None))
+        g = inplace_func(i, o, mode=compile.Mode(linker='py', optimizer=None))
         for node in g.maker.env.nodes:
             if node.op == gemm: raise Exception('gemm in original graph')
 
@@ -320,11 +320,11 @@ def test_gemm_opt_double_gemm():
     i = [X,Y,Z,a,b, R, S, c]
     o = [a * T.dot(X,Y) + gemm(Z, b, S.T, R.T, 1.0)]
     try:
-        f = function([In(ii, mutable=True) for ii in i],o, mode='FAST_RUN')
+        f = inplace_func([In(ii, mutable=True) for ii in i],o, mode='FAST_RUN')
         for node in f.maker.env.nodes:
             if node.op == T.dot: raise Failure('dot in graph')
             if node.op == _dot22: raise Failure('_dot22 in graph')
-        g = function(i, o, mode=compile.Mode(linker='py', optimizer=None))
+        g = inplace_func(i, o, mode=compile.Mode(linker='py', optimizer=None))
         #for node in g.maker.env.nodes:
         #    if node.op == gemm: raise Failure('gemm in graph')
 
@@ -379,11 +379,11 @@ def test_gemm_opt_vector_stuff():
     X,Y,Z,a,b = T.dmatrix(), T.dmatrix(), T.dmatrix(), T.dscalar(), T.dscalar()
     u,v = T.dvector(), T.dvector()
 
-    f = function([a, u, v], a + T.dot(u,v), mode='FAST_RUN')
+    f = inplace_func([a, u, v], a + T.dot(u,v), mode='FAST_RUN')
     if gemm in [n.op for n in f.maker.env.nodes]:
         raise Failure('gemm in graph')
     
-    f = function([a, u, X,Y], a * u + T.dot(X,Y), mode='FAST_RUN')
+    f = inplace_func([a, u, X,Y], a * u + T.dot(X,Y), mode='FAST_RUN')
     if (gemm in [n.op for n in f.maker.env.nodes]):
         raise Failure('gemm in graph')
 
@@ -392,7 +392,7 @@ def test_inplace0():
     X,Y,Z,a,b = T.dmatrix(), T.dmatrix(), T.dmatrix(), T.dscalar(), T.dscalar()
     R, S, c = T.dmatrix(), T.dmatrix(), T.dscalar()
 
-    f = function([X,Y,Z,a,b, R, S, c],
+    f = inplace_func([X,Y,Z,a,b, R, S, c],
             [Z * (Z *c + a * T.dot(X,Y) + b * T.dot(R,S).T)], mode='FAST_RUN')
     if (gemm in [n.op for n in f.maker.env.nodes]):
         raise Failure('gemm in graph')
@@ -400,7 +400,7 @@ def test_inplace0():
 def test_inplace1():
     X,Y,Z,a,b = XYZab()
     # with > 2 terms in the overall addition
-    f = function([X,Y,Z,a,b],
+    f = inplace_func([X,Y,Z,a,b],
             [Z + Z + T.dot(X,Y)], mode='FAST_RUN')
     if (gemm in [n.op for n in f.maker.env.nodes]):
         raise Failure('gemm in graph')

theano/tensor/tests/test_gc.py

@@ -73,7 +73,7 @@ def test_merge_opt_runtime():
         else:
             r = x
     t = time.time()
-    f = theano.function([x], r,mode='FAST_COMPILE')
+    f = theano.function([x], r)
     dt = time.time() - t
 
     assert dt < 5.0 #it should never take longer than 5 seconds to compile this graph

theano/tensor/tests/test_naacl09.py

@@ -5,6 +5,7 @@ from theano import tensor as T
 from theano.tensor import nnet as NN
 import numpy as N
 from theano.compile import module
+from theano.compile.mode import default_mode
 from theano import tensor as T, sparse as S
 import numpy as N
 import sys
@@ -482,15 +483,17 @@ def create_realistic(window_size=3,#7,
     model = architecture.make(input_size=input_dimension, input_representation_size=token_representation_size, hidden_representation_size=concatenated_representation_size, output_size=output_vocabsize, lr=lr, seed=seed, noise_level=noise_level, qfilter_relscale=qfilter_relscale, mode=compile_mode)
     return model
 
-def test_naacl_model(optimizer='fast_run', iters_per_unsup=10, iters_per_sup=10,
-        realistic=False):
+def test_naacl_model(iters_per_unsup=10, iters_per_sup=10,
+        optimizer=None, realistic=False):
     print "BUILDING MODEL"
     import time
     t = time.time()
+    
+    mode = theano.Mode(linker='c|py', optimizer=optimizer) if optimizer else default_mode
     if realistic:
-        m = create_realistic(compile_mode = theano.Mode(linker='c|py', optimizer=optimizer))
+        m = create_realistic(compile_mode=mode)
     else:
-        m = create(compile_mode = theano.Mode(linker='c|py', optimizer=optimizer))
+        m = create(compile_mode=mode)
 
     print 'BUILD took', time.time() - t
     prog_str = []

theano/tensor/tests/test_opt.py

@@ -136,7 +136,7 @@ class test_greedy_distribute(unittest.TestCase):
                 , eps + y/s
                 , s)
 
-        f = function([s, eps, x,y], r**2, mode=DebugMode())
+        f = function([s, eps, x,y], r**2)
 
         r0 = f(4,1.e-6, [1.5,2], [2.3,3.1])
         r1 = f(4,1.e-6, [1.5,2], [2.3,3.1])

theano/tensor/tests/test_raw_random.py

@@ -13,7 +13,7 @@ class T_test_module(unittest.TestCase):
     def test_state_propagation(self):
         x = tensor.vector()
         rk = RandomKit('rk', 1000)
-        f = compile.function([x, (rk, [gof.Container(r = gof.generic, storage = [123], name='bla')])], rk.binomial(tensor.shape(x)), mode='FAST_COMPILE')
+        f = compile.function([x, (rk, [gof.Container(r = gof.generic, storage = [123], name='bla')])], rk.binomial(tensor.shape(x)))
         f['rk'] = 9873456
     
         rvals = [f([1,2,3,4,6, 7, 8]) for i in xrange(5)]
@@ -45,7 +45,7 @@ class T_test_module(unittest.TestCase):
                 self.f = compile.Method([self.b.x], self.b.r)
 
         b = E()
-        m = b.make(mode='FAST_COMPILE')
+        m = b.make()
         
         m.seed(1000)
     #print m.f(N.ones(5))