implemented a hidden hack in profilemode to print the number of MFlops. Need…

theano/compile/profilemode.py

@@ -9,8 +9,10 @@ class ProfileMode(Mode):
     def __init__(self, linker=OpWiseCLinker(), optimizer=None):
         local_time = [0.0]
         apply_time = {}
+        apply_call = {}
         op_time = {}
         op_cimpl = {}
+        op_call = {}
 
         def blah(i, node, th):
             if hasattr(th, 'cthunk'):
@@ -24,13 +26,17 @@ class ProfileMode(Mode):
 
             local_time[0] += dt
             apply_time[(i,node.op)] = apply_time.get((i,node.op), 0.0) + dt
+            apply_call[(i,node.op)] = apply_call.get((i,node.op), 0) + 1
             op_time[node.op] = op_time.get(node.op, 0.0) + dt
             op_cimpl[node.op] = hasattr(th, 'cthunk')
+            op_call[node.op] = op_call.get(node.op,0) + 1
 
         self.local_time = local_time
         self.apply_time = apply_time
+        self.apply_call = apply_call
         self.op_time = op_time
         self.op_cimpl = op_cimpl
+        self.op_call = op_call
         self.compile_time = 0 #time passed in function()
 
         if isinstance(linker, str):
@@ -49,6 +55,8 @@ class ProfileMode(Mode):
         The Op-wise summary print the execution time of all Apply nodes executing the same Op are grouped together and the total execution time per Op is shown (so if you use dot twice, you will see only one entry there corresponding to the sum of the time spent in each of them). If two Op have different hash value, they will be separate.
         The type-Op-wise summary group the result by type of op. So event if two Op have different hash value, they will be merged.
 
+        Their is an hack with the Op-wise summary. Go see it if you want to know more.
+
         param: n_apply_to_print the number of apply to print. Default 15.
 
         param: n_ops_to_print the number of ops to print. Default 20.
@@ -75,16 +83,32 @@ class ProfileMode(Mode):
                   sum(f for f, t, a in atimes[n_apply_to_print:])*100,
                   sum(t for f, t, a in atimes[n_apply_to_print:]))
 
+        flops=False
+        flops_msg=''
+        for a,t in op_time.items():
+            if hasattr(a,'flops'):
+                print "if true!"
+                flops=True
+                flops_msg=' <MFlops/s>'
+                print '\nHACK WARNING: we print the flops for some OP, but the logic don\' always work. You need to know the internal of Theano to make it work correctly. Otherwise don\'t use!'
+                break
+            
+        print '\nOp-wise summary: < of local_time spent on this kind of Op> <cumulative seconds> <self seconds>%s <Op name>'%(flops_msg)
             
-        print '\nOp-wise summary: <% of local_time spent on this kind of Op> <cumulative seconds> <self seconds> <Op name>'
         otimes = [(t/local_time, t, a, self.op_cimpl[a]) for a, t in op_time.items()]
         otimes.sort()
         otimes.reverse()
         tot=0
         for f,t,a,ci in otimes[:n_ops_to_print]:
             tot+=t
-            print '   %.2f%%  %.3fs  %.3fs  %s %s' % (f*100, tot, t, '*' if ci else ' ', a)
-        print '   ... (remaining %i Ops account for %.2f%%(%.2fs) of the runtime)'\
+            m=-1
+            if hasattr(a,'flops'):
+                m=a.flops*self.op_call[a]/t/1e6
+            if flops:
+                print '   %4.1f%%  %.3fs  %.3fs  %s %7.1f %s' % (f*100, tot, t, '*' if ci else ' ', m,a)
+            else:
+                print '   %4.1f%%  %.3fs  %.3fs  %s %s' % (f*100, tot, t, '*' if ci else ' ', a)
+        print '   ... (remaining %i Ops account for %6.2f%%(%.2fs) of the runtime)'\
                 %(max(0, len(otimes)-n_ops_to_print),
                   sum(f for f, t, a, ci in otimes[n_ops_to_print:])*100,
                   sum(t for f, t, a, ci in otimes[n_ops_to_print:]))