cleaned up code related to gemm bug fix

theano/tensor/blas.py

@@ -442,7 +442,8 @@ class GemmLocalOptimizer(LocalOptimizer):
         if not isinstance(exc, InconsistencyError):
             traceback.print_exc()
         else:
-            print 'GEMM caused cycle, forget it.'
+            #print 'GEMM caused cycle, forget it.'
+            pass
 
     @staticmethod
     def _as_scalar(res):
@@ -497,16 +498,15 @@ class GemmLocalOptimizer(LocalOptimizer):
     def beta_L_plus_alpha_M(beta, L, alpha, M, recurse_flip = True):
         #print 'BETA L + ALPHA M', beta, L, alpha, M, recurse_flip
         #EXPRESSION: (beta * L) + (alpha * M)
-        if True:
-            if res_is_a(L, T.sqrt):
-                print 'CLIENTS OF L', L, L.clients
 
         if res_is_a(M, _dot22, 1):
             Ml, Mr = M.owner.inputs
             rval = [gemm(L, alpha, Ml, Mr, beta)]
-            print 'GEMM 0', rval, beta, L, alpha, M
+            #print 'GEMM 0', rval, beta, L, alpha, M
             return rval
 
+        # this is False'd out because of inadequate testing.  
+        # TODO see ticket #237
         if False and res_is_a(M, gemm, 1):
             #EXPRESSION: (beta * L) + (alpha * (gemm(G, a, u, v, b)))
             #EXPRESSION: (beta * L) + alpha * (b * G) + alpha * a * dot(u, v)

theano/tensor/tests/test_blas.py

-
+import traceback
 import theano.tensor as T
 from ...gof import Env
 import numpy
@@ -258,11 +258,11 @@ 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')
         for node in f.maker.env.nodes:
-            if node.op == T.dot: raise Warning('dot in graph')
-            if node.op == _dot22: raise Warning('_dot22 in graph')
+            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))
         for node in g.maker.env.nodes:
-            if node.op == gemm: raise Warning('gemm in graph')
+            if node.op == gemm: raise Exception('gemm in original graph')
 
         rng = numpy.random.RandomState(234)
         r0 = f(*[rng.randn(*sh) for sh in ishapes])
@@ -275,9 +275,11 @@ def just_gemm(i, o, ishapes = [(4,3), (3,5), (4,5), (), ()]):
         for node in f.maker.env.toposort():
             print 'GRAPH', node
         raise
-    except Warning:
-        for node in f.maker.env.toposort():
-            print 'GRAPH', node
+    except Warning, e:
+        #for node in f.maker.env.toposort():
+        #    print 'GRAPH', node
+        print 'WARNING:', e
+        #traceback.print_exc()
 
 
 def test_gemm_opt0():

theano/tensor/tests/test_joseph.py

@@ -457,13 +457,7 @@ def create(window_size=3,
     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
 
-from theano import gof
-JTEST = theano.compile.mode.optdb.query(*sys.argv[2:])
-print 'JTEST', JTEST
-theano.compile.register_optimizer('JTEST', JTEST)
-
-if __name__ == '__main__':
-    optimizer = eval(sys.argv[1])
+def test_naacl_model(optimizer='fast_run'):
     m = create(compile_mode = theano.Mode(linker='c|py', optimizer=optimizer))
     prog_str = []
     idx_of_node = {}
@@ -488,11 +482,24 @@ if __name__ == '__main__':
     for i in xrange(10):
         for i in xrange(10):
             m.pretraining_update(*inputs)
-        print m.pretraining_update(*inputs)
+        s0, s1 = [str(i) for i in m.pretraining_update(*inputs)]
+        print s0, s1
+    if s0 + ' ' + s1 != '0.315775007436 0.132479386981':
+        raise ValueError('pretraining update values do not match')
     print 'FINETUNING GRAPH'
     print 'SUPERVISED PHASE COSTS (%s)'%optimizer
     for i in xrange(10):
         for i in xrange(10):
-            m.finetuning_update(*(inputs + [targets])) #the 0 is the target
-        print m.finetuning_update(*(inputs + [targets])) #the 0 is the target
+            m.finetuning_update(*(inputs + [targets]))
+        s0 = str(m.finetuning_update(*(inputs + [targets])))
+        print s0
+    if s0 != '15.8609933666':
+        raise ValueError('finetuning values do not match')
 
+if __name__ == '__main__':
+    from theano import gof
+    JTEST = theano.compile.mode.optdb.query(*sys.argv[2:])
+    print 'JTEST', JTEST
+    theano.compile.register_optimizer('JTEST', JTEST)
+    optimizer = eval(sys.argv[1])
+    test_naacl_model(optimizer)