ModuleCache works without the pkl file now, more robust to various errors

theano/__init__.py

@@ -147,3 +147,13 @@ def dot(l, r):
         raise NotImplementedError("Dot failed for the following reaons:", (e0, e1))
     return rval
 
+
+### 
+#   Set a default logger
+#
+import logging
+logging_default_handler = logging.StreamHandler()
+logging.getLogger("theano").addHandler(logging_default_handler)
+logging.getLogger("theano").setLevel(logging.WARNING)
+
+

theano/compile/tests/test_inplace_opt_for_value.py

@@ -81,6 +81,11 @@ class TanhRnn(Op):
     in which z[0] = z0.
 
     """
+    def __eq__(self, other):
+        return (type(self) == type(other))
+
+    def __hash__(self):
+        return hash(type(self))
 
     def make_node(self, x, z0, A):
         """
@@ -121,7 +126,7 @@ class TanhRnnGrad(Op):
     def __eq__(self, other):
         return (type(self) == type(other))
 
-    def __hash__(self, other):
+    def __hash__(self):
         return hash(type(self))
 
     def make_node(self, A, z, gz):

theano/gof/cc.py

@@ -26,10 +26,10 @@ import cmodule
 import logging
 _logger=logging.getLogger("theano.gof.cc")
 def info(*args):
-    sys.stderr.write('INFO:'+ ' '.join(str(a) for a in args)+'\n')
+    #sys.stderr.write('INFO:'+ ' '.join(str(a) for a in args)+'\n')
     _logger.info(' '.join(str(a) for a in args))
 def debug(*args):
-    sys.stderr.write('DEBUG:'+ ' '.join(str(a) for a in args)+'\n')
+    #sys.stderr.write('DEBUG:'+ ' '.join(str(a) for a in args)+'\n')
     _logger.debug(' '.join(str(a) for a in args))
 def warning(*args):
     sys.stderr.write('WARNING:'+ ' '.join(str(a) for a in args)+'\n')
@@ -367,6 +367,7 @@ class CLinker(link.Linker):
         # The orphans field is listified to ensure a consistent order.
         self.orphans = list(r for r in self.variables if isinstance(r, graph.Value) and r not in self.inputs) #list(env.orphans.difference(self.outputs))
         self.temps = list(set(self.variables).difference(self.inputs).difference(self.outputs).difference(self.orphans))
+        self.consts = []
         self.node_order = env.toposort()
 
     def code_gen(self):
@@ -390,7 +391,7 @@ class CLinker(link.Linker):
 
         env = self.env
 
-        consts = []
+        self.consts = []
 
         symbol = {}
 
@@ -428,7 +429,7 @@ class CLinker(link.Linker):
                 if isinstance(variable, graph.Constant):
                     try:
                         symbol[variable] = "(" + variable.type.c_literal(variable.data) + ")"
-                        consts.append(variable)
+                        self.consts.append(variable)
                         self.orphans.remove(variable)
                         continue
                     except (utils.MethodNotDefined, NotImplementedError):
@@ -530,7 +531,12 @@ class CLinker(link.Linker):
         self.tasks = tasks
         all = self.inputs + self.outputs + self.orphans
 
-        assert (self.init_tasks, self.tasks) == self.get_init_tasks()
+        if (self.init_tasks, self.tasks) != self.get_init_tasks():
+            print >> sys.stderr, "init_tasks\n", self.init_tasks
+            print >> sys.stderr, self.get_init_tasks()[0]
+            print >> sys.stderr, "tasks\n", self.tasks
+            print >> sys.stderr, self.get_init_tasks()[1]
+            assert (self.init_tasks, self.tasks) == self.get_init_tasks()
 
         # List of indices that should be ignored when passing the arguments
         # (basically, everything that the previous call to uniq eliminated)
@@ -646,6 +652,14 @@ class CLinker(link.Linker):
         tasks = []
         id=1
         for v in self.variables:
+            if v in self.consts:
+                continue
+            if v in self.orphans and isinstance(v, graph.Constant):
+                try:
+                    v.type.c_literal(v.data) #constant will be inlined, no need to get
+                    continue 
+                except (utils.MethodNotDefined, NotImplementedError):
+                    pass
             init_tasks.append((v, 'init', id))
             tasks.append((v, 'get', id+1))
             id += 2
@@ -687,7 +701,7 @@ class CLinker(link.Linker):
 
         The signature has the following form:
         {{{
-            'CLinker.cmodule_key',
+            'CLinker.cmodule_key', compilation args, libraries,
             op0, (input0.type, input1.type, input0 pos, input1 pos)
             op1, (...)
             ...
@@ -717,6 +731,9 @@ class CLinker(link.Linker):
         env_computed_set = set()
         op_pos = {} # Apply -> topological position
         rval = ['CLinker.cmodule_key'] # will be cast to tuple on return
+        rval.append(tuple(self.compile_args()))
+        rval.append(tuple(self.libraries()))
+        version = []
 
         # assert that every input to every node is one of'
         # - an env input
@@ -735,12 +752,19 @@ class CLinker(link.Linker):
                 return (op_pos[i.owner], i.owner.outputs.index(i))
 
         for opos, o in enumerate(order):
+            version.append(o.op.c_code_cache_version())
+            for i in o.inputs:
+                version.append(i.type.c_code_cache_version())
+            for i in o.outputs:
+                version.append(i.type.c_code_cache_version())
             rval.append((o.op, tuple((i.type, graphpos(i)) for i in o.inputs)))
             op_pos[o] = opos
             env_computed_set.update(o.outputs)
 
-        rval = tuple(rval)
-        return rval
+        for v in version:
+            if not v: #one of the ops or types here is unversioned
+                return ((), tuple(rval))
+        return tuple(version), tuple(rval)
 
     def compile_cmodule(self, location=None):
         """

theano/gof/op.py

@@ -162,6 +162,16 @@ class CLinkerOp(object):
         raise utils.MethodNotDefined('%s.c_support_code' \
                 % self.__class__.__name__)
 
+    def c_code_cache_version(self):
+        """Return a tuple of integers indicating the version of this Op.
+
+        An empty tuple indicates an 'unversioned' Op that will not be cached between processes.
+
+        The cache mechanism may erase cached modules that have been superceded by newer
+        versions.  See `ModuleCache` for details.
+        """
+        return (1,)
+
 class PureOp(object):
     """
     An :term:`Op` is a type of operation.

theano/gof/tests/test_cc.py

@@ -57,6 +57,9 @@ class TDouble(Type):
             free(%(name)s_bad_thing);
         """ % locals()
 
+    def c_code_cache_version(self):
+        return ()
+
 tdouble = TDouble()
 
 def double(name):
@@ -83,6 +86,8 @@ class MyOp(Op):
     
     def perform(self, node, inputs, (out, )):
         out[0] = self.impl(*inputs)
+    def c_code_cache_version(self):
+        return ()
 
 
 class Unary(MyOp):

theano/gof/type.py

@@ -210,6 +210,16 @@ class CLinkerType(object):
         """
         raise MethodNotDefined("c_support_code", type(self), self.__class__.__name__)
 
+    def c_code_cache_version(self):
+        """Return a tuple of integers indicating the version of this Op.
+
+        An empty tuple indicates an 'unversioned' Op that will not be cached between processes.
+
+        The cache mechanism may erase cached modules that have been superceded by newer
+        versions.  See `ModuleCache` for details.
+        """
+        return (1,)
+
 class PureType(object):
     """Interface specification for variable type instances.
 

theano/sparse/basic.py

@@ -444,6 +444,10 @@ class DenseFromSparse(gof.op.Op):
     """
     sparse_grad = True
     """WRITEME"""
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
 
     def make_node(self, x):
         x = as_sparse_variable(x)
@@ -495,6 +499,10 @@ csc_from_dense = SparseFromDense('csc')
 class Transpose(gof.op.Op):
     format_map = {'csr' : 'csc',
                   'csc' : 'csr'}
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, x):
         x = as_sparse_variable(x)
         return gof.Apply(self,
@@ -510,6 +518,10 @@ class Transpose(gof.op.Op):
 transpose = Transpose()
 
 class Neg(gof.op.Op):
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, x):
         x = as_sparse_variable(x)
         return gof.Apply(self, [x], [x.type()])
@@ -523,6 +535,10 @@ neg = Neg()
 
 class AddSS(gof.op.Op):
     '''Add two sparse matrices '''
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, x, y):
         x, y = map(as_sparse_variable, [x, y])
         if x.type.dtype != y.type.dtype:
@@ -545,6 +561,10 @@ class AddSS(gof.op.Op):
 add_s_s = AddSS()
 class AddSD(gof.op.Op):
     ''' Add a sparse and a dense matrix '''
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, x, y):
         x, y = as_sparse_variable(x), tensor.as_tensor_variable(y)
         if x.type.dtype != y.type.dtype:
@@ -586,6 +606,10 @@ def sub(x,y):
 
 class MulSS(gof.op.Op):
     ''' Elementwise multiply a sparse and a ndarray '''
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, x, y):
         x, y = as_sparse_variable(x), as_sparse_variable(y)
         if x.type != y.type:
@@ -605,6 +629,10 @@ class MulSS(gof.op.Op):
 mul_s_s = MulSS()
 class MulSD(gof.op.Op):
     ''' Elementwise multiply a sparse and a ndarray '''
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, x, y):
         x, y = as_sparse_variable(x), tensor.as_tensor_variable(y)
         if x.type.dtype != y.type.dtype:
@@ -686,6 +714,10 @@ class StructuredDot(gof.Op):
 
     The output is presumed to be a dense matrix, and is represented by a TensorType instance.
     """
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, a, b):
         if type(a) is not SparseVariable and type(a) is not SparseConstant:
             raise TypeError('First argument must be of type SparseVariable or SparseConstant');
@@ -750,6 +782,10 @@ def structured_dot(x, y):
         return _structured_dot(y.T, x.T).T
 
 class StructuredDotCSC(gof.Op):
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, a_val, a_ind, a_ptr, a_nrows, b):
         dtype_out = scalar.upcast(a_val.type.dtype, b.type.dtype)
         r = gof.Apply(self, [a_val, a_ind, a_ptr, a_nrows, b], 
@@ -900,6 +936,10 @@ class StructuredDotCSC(gof.Op):
 sd_csc = StructuredDotCSC()
 
 class StructuredDotCSR(gof.Op):
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, a_val, a_ind, a_ptr, b):
         self.dtype_out = scalar.upcast(a_val.type.dtype, b.type.dtype)
         r = gof.Apply(self, [a_val, a_ind, a_ptr, b], 
@@ -1055,6 +1095,10 @@ def structured_dot_grad(sparse_A, dense_B, ga):
 
 
 class StructuredDotGradCSC(gof.Op):
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def make_node(self, a_indices, a_indptr, b, g_ab):
         return gof.Apply(self, [a_indices, a_indptr, b, g_ab], 
                                [tensor.tensor(g_ab.dtype, (False,))])
@@ -1155,6 +1199,10 @@ sdg_csc = StructuredDotGradCSC()
 
 
 class StructuredDotGradCSR(gof.Op):
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
 
     def make_node(self, a_indices, a_indptr, b, g_ab):
         return gof.Apply(self, [a_indices, a_indptr, b, g_ab], [tensor.tensor(b.dtype, (False,))])
@@ -1256,3 +1304,4 @@ class StructuredDotGradCSR(gof.Op):
  
         """% dict(locals(), **sub)
 sdg_csr = StructuredDotGradCSR()
+

theano/tensor/blas.py

@@ -49,6 +49,10 @@ class GemmRelated(Op):
     
     This class provides a kind of templated gemm Op.
     """
+    def __eq__(self, other):
+        return (type(self) == type(other))
+    def __hash__(self):
+        return hash(type(self))
     def c_support_code(self):
         #return cblas_header_text()
         mod_str = """