Make elemwise inplace profiler be well done.

theano/gpuarray/opt.py

@@ -743,7 +743,7 @@ optdb.register('gpua_elemwise_fusion',
                tensor.opt.FusionOptimizer(gpu_local_elemwise_fusion), 49,
                'fast_run', 'fusion', 'local_elemwise_fusion', 'gpuarray')
 
-inplace_gpu_elemwise_opt = tensor.opt.inplace_elemwise_optimizer_op(
+inplace_gpu_elemwise_opt = tensor.opt.InplaceElemwiseOptimizer(
     GpuElemwise)
 optdb.register('gpua_inplace_opt', inplace_gpu_elemwise_opt, 75,
                'inplace_elemwise_optimizer', 'fast_run', 'inplace', 'gpuarray')

theano/sandbox/cuda/opt.py

@@ -2181,7 +2181,7 @@ else:
                    71.00, 'fusion', 'local_elemwise_fusion')
 
 # GpuElemwise inplace
-gpu_inplace_elemwise_optimizer = tensor.opt.inplace_elemwise_optimizer_op(
+gpu_inplace_elemwise_optimizer = tensor.opt.InplaceElemwiseOptimizer(
     GpuElemwise)
 # DO NOT PLACE add a 'gpu' tag here! This would enable it in fast_compile.
 # It still will be run in fast_run with device=gpu with the current tag.

theano/tensor/opt.py

@@ -23,7 +23,7 @@ from theano import gof
 from theano.compat import izip
 from theano.gof import opt, InconsistencyError, TopoOptimizer, graph
 from theano.gof import Variable, Constant
-from theano.gof.opt import copy_stack_trace, in2out
+from theano.gof.opt import copy_stack_trace, in2out, Optimizer
 from theano.gof.utils import MethodNotDefined
 from theano.gradient import DisconnectedType
 from theano.configparser import config
@@ -147,14 +147,34 @@ def broadcast_like(value, template, fgraph, dtype=None):
     return rval
 
 
-def inplace_elemwise_optimizer_op(OP):
+class InplaceElemwiseOptimizer(Optimizer):
     """
     We parametrise it to make it work for Elemwise and GpuElemwise op.
     """
-    @gof.inplace_optimizer
-    def inplace_elemwise_optimizer(fgraph):
+    def __init__(self, OP):
+        self.OP = OP
+
+    def add_requirements(self, fgraph):
+        fgraph.attach_feature(theano.gof.destroyhandler.DestroyHandler())
+
+    @staticmethod
+    def print_profile(stream, prof, level=0):
+        blanc = ('    ' * level)
+        print(blanc, "InplaceElemwiseOptimizer ", prof['opt'].OP, file=stream)
+        for k in ['node_before',
+                  'nb_call_replace',
+                  'nb_call_validate',
+                  'nb_inconsistent']:
+            print(blanc, k, prof[k], file=stream)
+        ndim = prof['ndim']
+        if ndim:
+            print(blanc, "ndim", "nb", file=stream)
+            for n in sorted(ndim.keys()):
+                print(blanc, n, ndim[n], file=stream)
+
+    def apply(self, fgraph):
         """
-        Usage: inplace_elemwise_optimizer.optimize(fgraph)
+        Usage: InplaceElemwiseOptimizer(OP).optimize(fgraph)
 
         Attempts to replace all Broadcast ops by versions of them
         that operate inplace. It operates greedily: for each Broadcast
@@ -188,7 +208,8 @@ def inplace_elemwise_optimizer_op(OP):
         # the solution is also applicable there.
 
         # We execute `validate` after this number of change.
-        prof = {'node_before': len(fgraph.apply_nodes),
+        prof = {'opt': self,
+                'node_before': len(fgraph.apply_nodes),
                 'nb_call_replace': 0,
                 'nb_call_validate': 0,
                 'nb_inconsistent': 0,
@@ -217,7 +238,7 @@ def inplace_elemwise_optimizer_op(OP):
         for node in list(graph.io_toposort(fgraph.inputs, fgraph.outputs)):
             op = node.op
             # gpuarray GpuElemwise inherit from Elemwise
-            if not type(op) == OP:
+            if not type(op) == self.OP:
                 continue
             # If big graph and the outputs are scalar, do not make it
             # inplace.
@@ -334,7 +355,7 @@ def inplace_elemwise_optimizer_op(OP):
                                 scalar.transfer_type(
                                     *[inplace_pattern.get(i, None)
                                       for i in xrange(len(node.outputs))]))
-                        new_outputs = OP(new_scal, inplace_pattern)(
+                        new_outputs = self.OP(new_scal, inplace_pattern)(
                             *node.inputs, **dict(return_list=True))
                         new_node = new_outputs[0].owner
 
@@ -343,7 +364,7 @@ def inplace_elemwise_optimizer_op(OP):
                             fgraph.replace(r, new_r,
                                            reason="inplace_elemwise_optimizer")
                         nb_change_no_validate += 1
-                        prof['ndim'][node.ndim] += 1
+                        prof['ndim'][candidate_out_var.ndim] += 1
                         if nb_change_no_validate >= check_each_change:
                             prof['nb_call_validate'] += 1
                             fgraph.validate()
@@ -373,10 +394,11 @@ def inplace_elemwise_optimizer_op(OP):
                            "performed due to unexpected error"),
                           file=sys.stderr)
                 fgraph.revert(chk)
-        print(prof)
+        return prof
+        def print_summary(self, stream=sys.stdout, level=0, depth=-1):
             return inplace_elemwise_optimizer
 
-inplace_elemwise_optimizer = inplace_elemwise_optimizer_op(T.Elemwise)
+inplace_elemwise_optimizer = InplaceElemwiseOptimizer(T.Elemwise)
 compile.optdb.register('inplace_elemwise_opt', inplace_elemwise_optimizer, 75,
                        'inplace_opt',  # for historic reason
                        'inplace_elemwise_optimizer',