merge

theano/compile/debugmode.py

@@ -463,7 +463,12 @@ def _is_function_output(node):
 def _is_used_in_graph(node):
     return not(_is_function_output(node) or node.clients==[])
 
-def _check_strides_match(a, b, raise_on_err, op):
+def _check_strides_match(a, b, warn_err, op):
+    """
+    param: warn_err: if 0, no warning, if 1 warning, if 2 error
+    """
+    if warn_err==0: return
+
     try:
         strides_eq = a.strides == b.strides
     except:
@@ -471,7 +476,7 @@ def _check_strides_match(a, b, raise_on_err, op):
 
     if not strides_eq:
         e = TypeError('Stride mismatch', (a.shape, b.shape, a.strides, b.strides, str(op)))
-        if raise_on_err:
+        if warn_err==2:
             raise e
         else:
             print >> sys.stderr, 'WARNING:', e
@@ -1370,10 +1375,10 @@ class DebugMode(Mode):
     Should we check for (and complain about) NaN/Inf ndarray elements?
     """
 
-    require_matching_strides = bool(int(os.getenv('THEANO_DEBUGMODE_CHECK_STRIDES', 0)))
+    require_matching_strides = bool(int(os.getenv('THEANO_DEBUGMODE_CHECK_STRIDES', 1)))
     """
     Should we check for (and complain about) Ops whose python and C outputs are ndarrays with
-    different strides? (This can catch bugs, but is generally overly strict.)
+    different strides? (This can catch bugs, but is generally overly strict.) 0 no check, 1 warn, 2 err.
     """
 
     # This function will be used to create a FunctionMaker in 

theano/tensor/basic.py

@@ -2570,6 +2570,8 @@ def tile(x, reps, ndim=None):
 class Dot(Op):
     """Compute matrix-matrix, matrix-vector products and vector inner-products.
 
+    :note: matrix-matrix products are sometimes optimized to Dot22 ops (see tensor.blas)
+
     """
     def make_node(self, *inputs):
         inputs = map(as_tensor_variable, inputs)
@@ -2613,6 +2615,8 @@ class Dot(Op):
 
     def perform(self, node, (x, y), (z, )):
         try:
+            # the asarray is here because dot between two vectors gives a numpy float object
+            # but we need to return a 0d ndarray
             z[0] = numpy.asarray(numpy.dot(x, y))
         except ValueError, e:
             # The error raised by numpy has no shape information, we mean to add that

theano/tensor/opt.py

@@ -10,7 +10,6 @@ from elemwise import Elemwise, DimShuffle
 from theano import scalar
 import basic as T
 import inplace as I
-import numpy
 import numpy as N
 import operator
 import itertools
@@ -873,9 +872,9 @@ def local_mul_zero(node):
             except TypeError:
                 continue
             #print 'MUL by value', value, node.inputs
-            if numpy.all(value == 0):
+            if N.all(value == 0):
                 #print '... returning zeros'
-                return _fill_chain(numpy.asarray(0, dtype=otype.dtype), node.inputs)
+                return _fill_chain(N.asarray(0, dtype=otype.dtype), node.inputs)
 register_canonicalize(local_mul_zero)
 
 @gof.local_optimizer([T.true_div])
@@ -982,7 +981,7 @@ def local_mul_specialize(node):
         return False
 register_specialize(local_mul_specialize)
 
-@gof.local_optimizer([T.mul])
+@gof.local_optimizer([T.add])
 def local_add_specialize(node):
     def fill_chain(v):
         return _fill_chain(v, node.inputs)

theano/tensor/tests/test_opt.py

@@ -185,14 +185,58 @@ class test_canonize(unittest.TestCase):
         """
         verify that the Canonizer merge sequential Elemwise({mul,add})
         """
-        x, y, z = matrices('xyz')
-        for g,n in [
-            (x+y+z,1),
-            (x*y*z,1),
-            (x*y*(x+y+z),2),            
-            ]:
-            f = compile.function([x,y,z], g, mode=compile.Mode(optimizer='fast_run'))
-            assert(len(f.maker.env.toposort())==n)
+        shp=(5,5)
+        fx, fy, fz = fmatrices('xyz')
+        dx, dy, dz = dmatrices('xyz')
+        fv = fvector('r').dimshuffle('x',0)
+        fxv = numpy.asarray(numpy.random.rand(*shp),dtype='float32')
+        fyv = numpy.asarray(numpy.random.rand(*shp),dtype='float32')
+        fzv = numpy.asarray(numpy.random.rand(*shp),dtype='float32')
+        dxv = numpy.asarray(numpy.random.rand(*shp),dtype='float32')
+        dyv = numpy.asarray(numpy.random.rand(*shp),dtype='float32')
+        dzv = numpy.asarray(numpy.random.rand(*shp),dtype='float32')
+        fvv = numpy.asarray(numpy.random.rand(shp[0]),dtype='float32').reshape(1,shp[0])
+        cases = [
+            (fx+fy,(fx,fy),(fxv,fyv),1,'float32'),
+            (fx*fy,(fx,fy),(fxv,fyv),1,'float32'),
+            (fx+fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (dx+dy+dz,(dx,dy,dz),(dxv,dyv,dzv),1,'float64'),
+            (fx*fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (dx*dy*dz,(dx,dy,dz),(dxv,dyv,dzv),1,'float64'),
+            (fx*fy*(fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
+            (dx*dy*(dx+dy+dz),(dx,dy,dz),(dxv,dyv,dzv),2,'float64'),
+            (fx*fy*(fx+fy+dz),(fx,fy,dz),(dxv,dyv,dzv),2,'float64'),#check mixed type add
+            (dz*fy*(fx+fy),(fx,fy,dz),(dxv,dyv,dzv),2,'float64'),#check mixed type mul
+            #check with dimshuffle of constant
+            (fx+fy+fz+2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (fx*fy*fz*2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (2+fx+fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (2*fx*fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (2+fx+fy+fz+2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (2*fx*fy*fz*2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
+            (fx*fy*2*(fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
+            (fx*fy*(2+fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
+            (fx*fy*2*(fx+fy+fz+2),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
+
+            #check with broadcast of row
+            (fx+fy+fz+fv,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
+            (fx*fy*fz*fv,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
+            (fv+fx+fy+fz,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
+            (fv*fx*fy*fz,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
+            (fx*fy*fv*(fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
+            (fx*fy*(fv+fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
+            (fx*fy*fv*(fv+fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
+
+            ]#[10:11]
+#        print cases
+
+        for id, [g, sym_inputs, val_inputs, expected_out_nb_elemwise, out_dtype] in enumerate(cases):
+            f = compile.function(list(sym_inputs), g,
+                                 #we need the optimisation enabled, debug do this.
+                                 mode=compile.mode.predefined_modes['DEBUG_MODE'])
+            out = f(*val_inputs)
+            assert(len(f.maker.env.toposort())==expected_out_nb_elemwise)
+            assert(out_dtype==out.dtype)
 
 def test_mixeddiv():
     """Test that int division is preserved"""