many changes related to optimizations, deterministic ordering of optimizations, and benchmarking

benchmark/autoencoder/Makefile

 aa.x : aa.cc
-	g++ -O3 -ffast-math aa.cc -o aa.x -L${PUB_PREFIX}/lib -lgsl -lcblas -lgoto -lgfortran -lm
+	g++ -O3 -ffast-math -ftree-vectorize aa.cc -o aa.x -L${PUB_PREFIX}/lib -lgsl -lmkl
+	#g++ aa.cc -o aa.x -L${PUB_PREFIX}/lib -lgsl -lmkl
 
 clean : 
 	rm aa.x

benchmark/autoencoder/aa.cc

@@ -28,6 +28,7 @@ int main(int argc, char **argv)
 
     int neg = strtol(argv[1], 0, 0);
     int nout = strtol(argv[2], 0, 0);
+    int nin = nout;
     int nhid = strtol(argv[3], 0, 0);
     int niter = strtol(argv[4], 0, 0);
     double lr = 0.01;
@@ -35,8 +36,8 @@ int main(int argc, char **argv)
     gsl_rng_set(rng, 234);
 
 
-    gsl_matrix * x = gsl_matrix_alloc(neg, nout);
-    gsl_matrix * w = gsl_matrix_alloc(nout, nhid);
+    gsl_matrix * x = gsl_matrix_alloc(neg, nin);
+    gsl_matrix * w = gsl_matrix_alloc(nin, nhid);
     gsl_vector * a = gsl_vector_alloc(nhid);
     gsl_vector * b = gsl_vector_alloc(nout);
     gsl_matrix * xw = gsl_matrix_alloc(neg, nhid);
@@ -59,11 +60,17 @@ int main(int argc, char **argv)
 
     struct timeval tv0, tv1;
 
+    struct timeval tdot0, tdot1;
+    double time_of_dot = 0.0;
+
     gettimeofday(&tv0, 0);
     double err = 0.0;
     for (int iter = 0; iter < niter; ++iter)
     {
+        gettimeofday(&tdot0, 0);
         gsl_blas_dgemm(CblasNoTrans, CblasNoTrans, 1.0, x, w, 0.0, xw);
+        gettimeofday(&tdot1, 0);
+        time_of_dot += pytime(&tdot1) - pytime(&tdot0);
 
         for (int i = 0; i < neg; ++i)
             for (int j = 0; j < nhid; ++j)
@@ -72,7 +79,10 @@ int main(int argc, char **argv)
                 hid->data[i*nhid+j] = tanh(act);
             }
 
+        gettimeofday(&tdot0, 0);
         gsl_blas_dgemm(CblasNoTrans, CblasTrans, 1.0, hid, w, 0.0, hidwt);
+        gettimeofday(&tdot1, 0);
+        time_of_dot += pytime(&tdot1) - pytime(&tdot0);
 
         for (int i = 0; i < nout; ++i) g_b->data[i] = 0.0;
         err = 0.0;
@@ -90,8 +100,11 @@ int main(int argc, char **argv)
 
         if (1)
         {
+        gettimeofday(&tdot0, 0);
             gsl_blas_dgemm(CblasNoTrans, CblasNoTrans, 1.0, g_hidwt, w, 0.0, g_hid);
             gsl_blas_dgemm(CblasTrans, CblasNoTrans, 1.0, g_hidwt, hid, 0.0, g_w);
+        gettimeofday(&tdot1, 0);
+        time_of_dot += pytime(&tdot1) - pytime(&tdot0);
             
 
             for (int i = 0; i < neg; ++i)
@@ -101,14 +114,19 @@ int main(int argc, char **argv)
                     a->data[j] -= lr * g_hid->data[i*nhid+j];
                 }
 
+        gettimeofday(&tdot0, 0);
             gsl_blas_dgemm(CblasTrans, CblasNoTrans, -lr, x, g_hid, 1.0, w);
+        gettimeofday(&tdot1, 0);
+        time_of_dot += pytime(&tdot1) - pytime(&tdot0);
             for (int i = 0; i < nout*nhid; ++i) w->data[i] -= lr * g_w->data[i];
         }
 
     }
     gettimeofday(&tv1, 0);
 
-    fprintf(stdout, "took = %lfs  to get err %lf\n", pytime(&tv1) - pytime(&tv0), 0.5 * err);
+    double total_time = pytime(&tv1) - pytime(&tv0);
+    fprintf(stdout, "took = %lfs  to get err %lf\n", total_time, 0.5 * err);
+    fprintf(stdout, "... of which %.2lfs was spent in dgemm (fraction: %.2lf)\n", time_of_dot, time_of_dot / total_time);
     //skip freeing
     return 0;
 }

benchmark/autoencoder/aa.py

@@ -11,6 +11,9 @@ import theano.sandbox.wraplinker
 from theano.compile import module, Mode
 from theano.sandbox.wraplinker import ProfileMode
 
+# numpy: aa_numpy.py
+# c : aa.cc
+
 if 0:
     class Opt(object):
         merge = theano.gof.MergeOptimizer()
@@ -131,7 +134,7 @@ if 0:
 
                 self.merge(env)
 
-def linker(print_prog=True):
+def print_graph_linker(print_prog=True):
     if 1:
         imap = {None:'-'}
         def blah(i, node, thunk):
@@ -146,7 +149,6 @@ def linker(print_prog=True):
                 print 'node ', i, node,
                 print ':'.join([imap[inp.owner] for inp in node.inputs])
                 #print theano.sandbox.pprint.pp.process_graph(inputs, outputs)
-                
         return theano.sandbox.wraplinker.WrapLinkerMany(
                 [theano.gof.OpWiseCLinker()],
                 [theano.sandbox.wraplinker.run_all
@@ -184,8 +186,9 @@ class M(module.Module):
         self.step = module.Method([x], err, updates=dict(updates))
 
 mod = M()
-#m = mod.make(mode='FAST_RUN')
+#mode = 'FAST_RUN'
 mode = ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
+print mod.pretty(mode=mode)
 m = mod.make(mode=mode)
 
 neg, nout, nhid, niter = [int(a) for a in sys.argv[1:]]
@@ -200,5 +203,10 @@ t = time.time()
 for i in xrange(niter):
     err = m.step(x)
 print 'time: ',time.time() - t, 'err: ', err
-mode.print_summary()
+try:
+    mode.print_summary()
+    pass
+except:
+    raise
+
 

benchmark/autoencoder/aa_numpy.py

@@ -4,6 +4,8 @@ import numpy as N
 import sys
 import time
 
+# c: aa.cc
+
 neg, nout, nhid, niter = [int(a) for a in sys.argv[1:]]
 lr = 0.01
 
@@ -14,12 +16,20 @@ a = rng.randn(nhid) * 0.0
 b = rng.randn(nout) * 0.0
 x = (rng.rand(neg, nout)-0.5) * 1.5
 
+dot_time = 0.0
 
 t = time.time()
 for i in xrange(niter):
-    hid = N.tanh(N.dot(x, w) + a)
+    tt = time.time()
+    d = N.dot(x, w)
+    dot_time += time.time() - tt
+
+    hid = N.tanh(d + a)
 
-    out = N.tanh(N.dot(hid, w.T) + b)
+    tt = time.time()
+    d = N.dot(hid, w.T)
+    dot_time += time.time() - tt
+    out = N.tanh(d + b)
 
     g_out = out - x
     err = 0.5 * N.sum(g_out**2)
@@ -28,12 +38,23 @@ for i in xrange(niter):
 
     b -= lr * N.sum(g_hidwt, axis=0)
 
+    tt = time.time()
     g_hid = N.dot(g_hidwt, w)
+    dot_time += time.time() - tt
+
     g_hidin = g_hid * (1.0 - hid**2)
 
-    w -= lr * (N.dot(g_hidwt.T, hid) + N.dot(x.T, g_hidin))
+    tt = time.time()
+    d = N.dot(g_hidwt.T, hid)
+    dd = N.dot(x.T, g_hidin)
+    dot_time += time.time() - tt
+
+    gw = (d + dd)
+    w -= lr * gw
 
     a -= lr * N.sum(g_hidin, axis=0)
 
-print 'time: ',time.time() - t, 'err: ', err
+total_time = time.time() - t
+print 'time: ',total_time, 'err: ', err
+print ' of which', dot_time, 'was spent on dot. Fraction:', dot_time / total_time
 

theano/gof/__init__.py

@@ -23,11 +23,12 @@ from op import \
 from opt import \
     Optimizer, optimizer, SeqOptimizer, \
     MergeOptimizer, MergeOptMerge, \
-    LocalOptimizer, local_optimizer, LocalOptGroup, LocalOpKeyOptGroup, \
+    LocalOptimizer, local_optimizer, LocalOptGroup, \
     OpSub, OpRemove, PatternSub, \
-    NavigatorOptimizer, TopoOptimizer, OpKeyOptimizer, EquilibriumOptimizer, \
+    NavigatorOptimizer, TopoOptimizer, EquilibriumOptimizer, \
     keep_going, warn, \
     InplaceOptimizer, PureThenInplaceOptimizer
+    #LocalOpKeyOptGroup, OpKeyOptimizer
 
 from optdb import \
     DB, Query, \

theano/gof/cc.py

@@ -736,6 +736,7 @@ def _execute(cthunk, init_tasks, tasks, error_storage):
         else:
             return tasks[failure_code - n]
     def execute():
+        execute.cthunk = cthunk
         failure = cutils.run_cthunk(cthunk)
         if failure:
             task, taskname, id = find_task(failure)

theano/gof/graph.py

@@ -13,6 +13,7 @@ from collections import deque
 
 import utils
 
+_creation_idx = [0]
 
 class Apply(utils.object2):
     """
@@ -121,6 +122,13 @@ class Apply(utils.object2):
     def __asapply__(self):
         return self
 
+    def __hash__(self):
+        if not hasattr(self, '_creation_idx'):
+            self._creation_idx = _creation_idx[0]
+            _creation_idx[0] += 1
+        return self._creation_idx
+
+
     def clone(self):
         """Duplicate this Apply instance with inputs = self.inputs.
 
@@ -567,7 +575,10 @@ def general_toposort(r_out, deps, debug_print = False):
         deps(i) should behave like a pure function (no funny business with internal state)
 
     :note: 
-        deps(i) can/should be cached by the deps function to be fast
+        deps(i) will be cached by this function (to be fast)
+
+    :note:
+        The order of the return value list is determined by the order of nodes returned by the deps() function.
     """
     deps_cache = {}
     def _deps(io):
@@ -611,6 +622,7 @@ def general_toposort(r_out, deps, debug_print = False):
 def io_toposort(i, o, orderings = {}):
     """WRITEME
     """
+    #the inputs are used only here in the function that decides what 'predecessors' to explore
     iset = set(i)
     def deps(obj): 
         rval = []

theano/gof/opt.py

@@ -17,6 +17,9 @@ import sys
 
 _optimizer_idx = [0]
 
+def _list_of_nodes(env):
+    return graph.io_toposort(env.inputs, env.outputs)
+
 class Optimizer(object):
     """WRITEME
     An L{Optimizer} can be applied to an L{Env} to transform it.
@@ -73,7 +76,7 @@ class FromFunctionOptimizer(Optimizer):
         env.extend(toolbox.ReplaceValidate())
 
 def optimizer(f):
-    """WRITEME"""
+    """decorator for FromFunctionOptimizer"""
     return FromFunctionOptimizer(f)
 
 
@@ -137,6 +140,10 @@ class _metadict:
         try:
             self.d[item] = value
         except:
+            for i, (key,val) in enumerate(self.l):
+                if key == item:
+                    self.l[i] = (item, value)
+                    return
             self.l.append((item, value))
     def get(self, item, default):
         try:
@@ -191,7 +198,7 @@ class MergeOptimizer(Optimizer):
             cid[r] = i
             inv_cid[i] = r
 
-        for node in graph.io_toposort(env.inputs, env.outputs):
+        for node in _list_of_nodes(env):
             node_cid = (node.op, tuple([cid[input] for input in node.inputs]))
             dup = inv_cid.get(node_cid, None)
             success = False
@@ -229,10 +236,33 @@ def MergeOptMerge(opt):
 ### Local Optimizers ###
 ########################
 
-class LocalOptimizer(Optimizer, utils.object2):
-    """WRITEME"""
+class LocalOptimizer(object):
+    """A class for node-based optimizations.
+
+    Instances should implement the transform function, 
+    and be passed to configure a env-based Optimizer instance.
+    """
+
+    def __hash__(self):
+        if not hasattr(self, '_optimizer_idx'):
+            self._optimizer_idx = _optimizer_idx[0]
+            _optimizer_idx[0] += 1
+        return self._optimizer_idx
 
     def transform(self, node):
+        """Transform a subgraph whose output is `node`.
+
+        Subclasses should implement this function so that it returns one of two
+        kinds of things:
+
+        - False to indicate that no optimization can be applied to this `node`; or
+
+        - <list of results> to use in place of `node`'s outputs in the greater graph.
+
+        :type node: an Apply instance
+
+        """
+
         raise utils.AbstractFunctionError()
 
 
@@ -272,7 +302,7 @@ class LocalOptGroup(LocalOptimizer):
                 return repl
 
 
-class LocalOpKeyOptGroup(LocalOptGroup):
+class _LocalOpKeyOptGroup(LocalOptGroup):
     """WRITEME"""
 
     def __init__(self, optimizers):
@@ -515,9 +545,29 @@ class PatternSub(LocalOptimizer):
 
 
 class NavigatorOptimizer(Optimizer):
-    """WRITEME"""
+    """Abstract class
+    
+    """
 
     def __init__(self, local_opt, ignore_newtrees = 'auto', failure_callback = None):
+        """
+        :param local_opt:  a LocalOptimizer to apply over a Env.
+        :param ignore_newtrees: 
+            - True: new subgraphs returned by an optimization is not a candidate for optimization
+            - False: new subgraphs returned by an optimization is a candidate for optimization
+            - 'auto': let the local_opt set this parameter via its 'reentrant' attribute.
+        :param failure_callback:
+            a function that takes (exception, navigator, [(old, new),
+            (old,new),...]) and we call it if there's an exception.
+              
+            If the trouble is from local_opt.transform(), the new variables will be 'None'.
+
+            If the trouble is from validation (the new types don't match for
+            example) then the new variables will be the ones created by
+            transform().
+
+            If this parameter is None, then exceptions are not caught here (raised normally).
+        """
         self.local_opt = local_opt
         if ignore_newtrees == 'auto':
             self.ignore_newtrees = not getattr(local_opt, 'reentrant', True)
@@ -526,6 +576,15 @@ class NavigatorOptimizer(Optimizer):
         self.failure_callback = failure_callback
 
     def attach_updater(self, env, importer, pruner, chin = None):
+        """Install some Env listeners to help the navigator deal with the ignore_trees-related functionality.
+
+        :param importer: function that will be called whenever when optimizations add stuff to the graph.
+        :param pruner: function to be called when optimizations remove stuff from graph.
+        :param chin: "on change input" called whenever an node's inputs change.
+
+        :returns: The Env plugin that handles the three tasks.  Keep this around so that you can detach later!
+
+        """
         if self.ignore_newtrees:
             importer = None
 
@@ -548,6 +607,12 @@ class NavigatorOptimizer(Optimizer):
         return u
 
     def detach_updater(self, env, u):
+        """Undo the work of attach_updater.
+
+        :param u: a return-value of attach_updater
+
+        :returns: None.
+        """
         if u is not None:
             env.remove_feature(u)
 
@@ -610,7 +675,7 @@ class TopoOptimizer(NavigatorOptimizer):
         except:
             self.detach_updater(env, u)
             raise
-        
+        self.detach_updater(env, u)
 
 
 class OpKeyOptimizer(NavigatorOptimizer):
@@ -642,6 +707,7 @@ class OpKeyOptimizer(NavigatorOptimizer):
         except:
             self.detach_updater(env, u)
             raise
+        self.detach_updater(env, u)
 
     def add_requirements(self, env):
         """
@@ -654,38 +720,70 @@ class OpKeyOptimizer(NavigatorOptimizer):
 
 
 
-# class EquilibriumOptimizer(NavigatorOptimizer):
-#     """WRITEME"""
-
-#     def __init__(self, local_optimizers, failure_callback = None):
-#         NavigatorOptimizer.__init__(self, local_opt, ignore_newtrees, failure_callback)
-    
-#     def apply(self, env):
-#         op = self.local_opt.op_key()
-#         if isinstance(op, (list, tuple)):
-#             q = reduce(list.__iadd__, map(env.get_nodes, op))
-#         else:
-#             q = list(env.get_nodes(op))
-#         def importer(node):
-#             if node.op == op: q.append(node)
-#         def pruner(node):
-#             if node is not current_node and node.op == op:
-#                 try: q.remove(node)
-#                 except ValueError: pass
-#         u = self.attach_updater(env, importer, pruner)
-#         try:
-#             while q:
-#                 node = q.pop()
-#                 current_node = node
-#                 self.process_node(env, node)
-#         except:
-#             self.detach_updater(env, u)
-#             raise
-
-
 from utils import D
 
 class EquilibriumOptimizer(NavigatorOptimizer):
+    def __init__(self,
+                 local_optimizers,
+                 failure_callback = None,
+                 max_depth = None,
+                 max_use_ratio = None):
+        """
+        :param max_use_ratio: each optimizer can be applied at most (size of graph * this number)
+
+        """
+
+        super(EquilibriumOptimizer, self).__init__(
+            None,
+            ignore_newtrees = True,
+            failure_callback = failure_callback)
+
+        self.local_optimizers = local_optimizers
+        self.max_depth = max_depth
+        self.max_use_ratio = max_use_ratio
+
+    def apply(self, env, start_from = None):
+        if start_from is None:
+            start_from = env.outputs
+        changed = True
+        max_use_abort = False
+        process_count = {}
+
+        while changed and not max_use_abort:
+            changed = False
+
+            q = deque(graph.io_toposort(env.inputs, start_from))
+
+            max_use = len(q) * self.max_use_ratio
+            def importer(node):
+                q.append(node)
+            def pruner(node):
+                if node is not current_node:
+                    try: q.remove(node)
+                    except ValueError: pass
+            
+            u = self.attach_updater(env, importer, pruner)
+            try:
+                while q:
+                    node = q.pop()
+                    current_node = node
+                    for lopt in self.local_optimizers:
+                        process_count.setdefault(lopt, 0)
+                        if process_count[lopt] > max_use:
+                            max_use_abort = True
+                        else:
+                            lopt_change = self.process_node(env, node, lopt)
+                            process_count[lopt] += 1 if lopt_change else 0
+                            changed |= lopt_change
+            except:
+                self.detach_updater(env, u)
+                raise
+            self.detach_updater(env, u)
+        if max_use_abort:
+            print >> sys.stderr, "WARNING: EquilibriumOptimizer max'ed out"
+
+
+class _EquilibriumOptimizer(NavigatorOptimizer):
 
     def __init__(self,
                  local_optimizers,
@@ -780,10 +878,11 @@ class EquilibriumOptimizer(NavigatorOptimizer):
 #             importer(node)
 
         
-        for node in env.nodes:
+        for node in env.toposort():
             tasks[node].extend(lopt for track, i, lopt in self.fetch_tracks0(node.op))
 
         u = self.attach_updater(env, importer, pruner, chin)
+        print 'KEYS', map(hash, tasks.keys())
         while tasks:
             for node in tasks.iterkeys():
                 todo = tasks.pop(node)

theano/gof/optdb.py

@@ -18,7 +18,7 @@ class DB(object):
         # N.B. obj is not an instance of class Optimizer.
         # It is an instance of a DB.In the tests for example,
         # this is not always the case.
-        if not isinstance(obj, (DB, opt.Optimizer)):
+        if not isinstance(obj, (DB, opt.Optimizer, opt.LocalOptimizer)):
             raise Exception('wtf', obj)
             
         obj.name = name

theano/gof/tests/test_opt.py

@@ -375,7 +375,7 @@ class TestEquilibrium(object):
         x, y, z = map(MyResult, 'xyz')
         e = op3(op4(x, y))
         g = Env([x, y, z], [e])
-        print g
+        print 'before', g
         sys.stderr = sys.stdout # display pesky warnings along with stdout
         opt = EquilibriumOptimizer(
             [PatternSub((op1, 'x', 'y'), (op2, 'x', 'y')),
@@ -384,7 +384,7 @@ class TestEquilibrium(object):
              ],
             max_use_ratio = 1. / len(g.nodes)) # each opt can only be applied once
         opt.optimize(g)
-        print g
+        print 'after', g
         assert str(g) == '[Op4(x, y)]'
 
 

theano/gradient.py

@@ -2,6 +2,7 @@ import gof #, gof.result
 import numpy #for numeric_grad
 
 from gof.python25 import all
+import gof.utils
 
 _msg_retType = 'op.grad(...) returned a non-list'
 _msg_badlen = 'op.grad(...) returned wrong number of gradients'
@@ -55,7 +56,7 @@ def grad_sources_inputs(sources, graph_inputs):
             else:
                 gmap[r] = g_r
 
-    graph_outputs = gmap.keys()
+    graph_outputs = gof.utils.uniq([r for r,g in sources])
 
     if graph_inputs is None:
         graph_inputs = gof.graph.inputs(graph_outputs)

theano/sandbox/wraplinker.py

@@ -2,6 +2,7 @@ from __future__ import absolute_import
 import time
 import numpy
 
+from ..gof.cutils import run_cthunk
 from ..gof.link import WrapLinker
 from ..compile.mode import Mode
 
@@ -107,19 +108,42 @@ class ProfileMode(Mode):
         local_time = [0.0]
         apply_time = {}
         op_time = {}
+        op_cimpl = {}
 
         def blah(i, node, *thunks):
+            if 0:
                 t0 = time.time() 
                 for th in thunks:
                     th()
                 dt = time.time() - t0
+            elif 0: #more precise timing
+                for th in thunks:
+                    t0 = time.time()
+                    th()
+                    dt = time.time() - t0
+            elif 1:
+                for th in thunks:
+                    if hasattr(th, 'cthunk'):
+                        t0 = time.time()
+                        run_cthunk(th.cthunk)
+                        dt = time.time() - t0
+                    else:
+                        t0 = time.time()
+                        th()
+                        dt = time.time() - t0
+            elif 1:
+                pass
+            else:
+                raise Exception('one of the cases has to run the thunks!')
             local_time[0] += dt
             apply_time[(i,node.op)] = apply_time.get((i,node.op), 0.0) + dt
             op_time[node.op] = op_time.get(node.op, 0.0) + dt
+            op_cimpl[node.op] = hasattr(thunks[0], 'cthunk')
 
         self.local_time = local_time
         self.apply_time = apply_time
         self.op_time = op_time
+        self.op_cimpl = op_cimpl
 
         wrap_linker = WrapLinkerMany([linker], [blah])
         if optimizer:
@@ -142,13 +166,18 @@ class ProfileMode(Mode):
         atimes.sort()
         atimes.reverse()
         for t,a in atimes[:15]:
-            print '  ', t, a
-        print '   ... (ignoring %i other Apply instances)'%max(0, len(atimes)-15)
+            print '\t%.3f\t%i\t%s' % (t, a[0], a[1])
+        print '   ... (remaining %i Apply instances account for %.2f of the runtime)'\
+                %(max(0, len(atimes)-15), sum(t for t, a in atimes[15:]))
+
+
         print 'Op-wise summary: <fraction of local_time spent on this kind of Op> <Op name>'
-        otimes = [(t/local_time, a) for a, t in op_time.items()]
+        otimes = [(t/local_time, a, self.op_cimpl[a]) for a, t in op_time.items()]
         otimes.sort()
         otimes.reverse()
-        for t,a in otimes[:15]:
-            print '  ', t, a
-        print '   ... (ignoring %i other kinds Ops)'%max(0, len(otimes)-15)
+        for t,a,ci in otimes[:15]:
+            print '\t%.3f\t%s %s' % (t, '*' if ci else ' ', a)
+        print '   ... (remaining %i Ops account for %.2f of the runtime)'\
+                %(max(0, len(otimes)-15), sum(t for t, a, ci in otimes[15:]))
+        print '(*) Op is running a c implementation'
 

theano/tensor/basic.py

@@ -1089,7 +1089,8 @@ pprint.assign(pow, printing.OperatorPrinter('**', 1, 'right'))
 # View Operations
 ##########################
 
-class TransposeInplace(Op):
+if 0:
+    class _TransposeInplace(Op):
         view_map = {0: [0]}
         
         def make_node(self, input):
@@ -1114,12 +1115,16 @@ class TransposeInplace(Op):
         def __str__(self):
             return "TransposeView"
 
-_transpose_inplace = TransposeInplace()
+    _transpose_inplace = _TransposeInplace()
 
-def transpose(x, **kwargs):
+    def _old_transpose(x, **kwargs):
         """WRITEME"""
         return _transpose_inplace(tensor_copy(x), **kwargs)
 
+def transpose(x, **kwargs):
+    dims = range(x.ndim-1, -1, -1)
+    return DimShuffle(x.broadcastable, dims, inplace=True)(tensor_copy(x))
+
 
 
 

theano/tensor/elemwise.py

@@ -181,7 +181,7 @@ class DimShuffle(Op):
         for i, v in enumerate(self.new_order):
             if v != 'x':
                 grad_order[v] = i
-        return DimShuffle(gz.type.broadcastable, grad_order)(gz),
+        return [DimShuffle(gz.type.broadcastable, grad_order, inplace=True)(Elemwise(scalar.identity)(gz))]
 
 
 

theano/tensor/inplace.py

 
-from basic import _scal_elemwise, _transpose_inplace
+from basic import _scal_elemwise #, _transpose_inplace
 from .. import scalar as scal
 import elemwise
 from .. import printing
@@ -183,9 +183,11 @@ pprint.assign(div_inplace, printing.OperatorPrinter('/=', -1, 'left'))
 pprint.assign(pow_inplace, printing.OperatorPrinter('**=', 1, 'right'))
 
 
-transpose_inplace = _transpose_inplace
-"""WRITEME"""
+def transpose_inplace(x, **kwargs):
+    """Perform a transpose on a tensor without copying the underlying storage"""
+    dims = range(x.ndim-1, -1, -1)
+    return elemwise.DimShuffle(x.broadcastable, dims, inplace=True)(x)
 
-pprint.assign(transpose_inplace, printing.MemberPrinter('T'))
+#pprint.assign(transpose_inplace, printing.MemberPrinter('T'))
 
 

theano/tensor/opt.py

@@ -53,6 +53,7 @@ dot_to_gemm = gof.PatternSub((T.dot, 'a', 'b'),
                               allow_multiple_clients = False)
 
 
+
 def _insert_inplace_optimizer(env):
     """
     Usage: inplace_optimizer.optimize(env)
@@ -216,6 +217,13 @@ register_canonicalize(local_shape_lift_dot)
 ################
 
 def encompasses_broadcastable(b1, b2):
+    """
+    Returns True if the broadcastable patterns b1 and b2 are such that b2 is
+    broadcasted to b1's shape and not the opposite.
+
+    :param b1: the broadcastable attribute of a tensor type
+    :param b2: the broadcastable attribute of a tensor type
+    """
     if len(b1) < len(b2):
         return False
     b1 = b1[-len(b2):]
@@ -330,6 +338,7 @@ def local_fill_cut(node):
 
 register_canonicalize(local_fill_cut)
 
+register_canonicalize(gof.OpRemove(T.tensor_copy), name='remove_tensor_copy' )
 
 @gof.local_optimizer([None, T.fill])
 def local_fill_sink(node):
@@ -550,6 +559,7 @@ def local_neg_to_mul(node):
         return [-1 * node.inputs[0]]
     else:
         return False
+register_canonicalize(local_neg_to_mul)
 
 @gof.local_optimizer([T.mul])
 def local_mul_to_neg(node):
@@ -557,6 +567,7 @@ def local_mul_to_neg(node):
         return [-local_mul_canonizer.merge_num_denum(node.inputs[1:], [])]
     else:
         return False
+register_specialize(local_mul_to_neg)
 
 @gof.local_optimizer([T.div])
 def local_div_to_inv(node):
@@ -564,10 +575,57 @@ def local_div_to_inv(node):
         return [T.inv(local_mul_canonizer.merge_num_denum(node.inputs[1:], []))]
     else:
         return False
-
-register_canonicalize(local_neg_to_mul)
-register_specialize(local_mul_to_neg)
 register_specialize(local_div_to_inv)
+
+@gof.local_optimizer([T.inv])
+def local_inv_canon(node):
+    if node.op == T.inv:
+        return [T.pow(node.inputs[0], -1.0)]
+    else:
+        return False
+register_canonicalize(local_inv_canon)
+
+@gof.local_optimizer([T.pow])
+def local_pow_canonicalize(node):
+    if node.op == T.pow:
+        if N.all(local_mul_canonizer.get_constant(node.inputs[1]) == 1.0):
+            return [T.fill(node.inputs[1], node.inputs[0])]
+        if N.all(local_mul_canonizer.get_constant(node.inputs[1]) == 0.0):
+            #extra fills here are to make sure the size of the output stays constant.
+            return [T.fill(node.inputs[0], T.fill(node.inputs[1], 1.0))]
+    else:
+        return False
+register_canonicalize(local_pow_canonicalize)
+
+@gof.local_optimizer([T.pow])
+def local_pow_specialize(node):
+    #here, we are past the point of canonicalization, so we don't want to put in un-necessary fills.
+    if node.op == T.pow:
+        #the idea here is that we have pow(x, y)
+        xsym = node.inputs[0]
+        ysym = node.inputs[1]
+        y = local_mul_canonizer.get_constant(ysym)
+        if (y is not None) \
+                and encompasses_broadcastable(xsym.type.broadcastable, ysym.type.broadcastable):
+            if N.all(y == 2.0):
+                return [T.sqr(xsym)]
+            if N.all(y == 1.0):
+                return [xsym]
+            if N.all(y == 0.0):
+                return [T.fill(xsym, 1.0)]
+            if N.all(y == 0.5):
+                return [T.sqrt(xsym)]
+            if N.all(y == -0.5):
+                return [T.inv(T.sqrt(xsym))]
+            if N.all(y == -1.0):
+                return [T.inv(xsym)]
+            if N.all(y == -2.0):
+                return [T.inv(T.sqr(xsym))]
+    else:
+        return False
+register_specialize(local_pow_specialize)
+
+
 register_canonicalize(local_mul_canonizer, name = 'local_mul_canonizer')
 
 

theano/tensor/tests/test_basic.py

@@ -662,56 +662,6 @@ class T_max_and_argmax(unittest.TestCase):
         self.failUnless(i.shape == (2,3))
 
 
-class T_transpose(unittest.TestCase):
-    def test0(self):
-        n = as_tensor(numpy.ones(()))
-        t = transpose(n)
-        self.failUnless(t.owner.op == inplace.transpose_inplace)
-        f = function([n], t)
-        tval = f(n.data)
-        self.failUnless(tval.shape == n.data.shape)
-
-        #test aliasing
-        tval += 55.0
-        self.failUnless(n.data == 1.0)
-
-    def test1(self):
-        n = as_tensor(numpy.ones(5))
-        t = transpose(n)
-        self.failUnless(t.owner.op == inplace.transpose_inplace)
-        f = function([n], t)
-        tval = f(n.data)
-        self.failUnless(tval.shape == n.data.shape)
-        #test aliasing
-        tval += 55.0
-        self.failUnless(n.data[0] == 1.0)
-
-    def test2(self):
-        n = as_tensor(numpy.ones((5,3)))
-        t = transpose(n)
-        self.failUnless(t.owner.op == inplace.transpose_inplace)
-        f = function([n], t)
-        tval = f(n.data)
-        self.failUnless(tval.shape == (3,5))
-        #test aliasing
-        tval += 55.0
-        self.failUnless(n.data[0,0] == 1.0)
-
-    def test3(self):
-        """Test transpose of tensor, inplace version"""
-        n = as_tensor(numpy.ones((5,3,2)))
-        t = inplace.transpose_inplace(n)
-        self.failUnless(t.owner.op == inplace.transpose_inplace)
-        f = function([n], t)
-        tval = f(n.data)
-        self.failUnless(tval.shape == (2,3,5))
-        #test aliasing
-        tval += 55.0
-        self.failUnless(n.data[0,0,0] == 56.0)
-    def test_grad(self):
-        verify_grad(self, inplace.transpose_inplace, [numpy.random.rand(2, 3)])
-        verify_grad(self, inplace.transpose_inplace, [numpy.ones(3)])
-
 class T_subtensor(unittest.TestCase):
     def setUp(self):
         Subtensor.debug = False