Add kind and context to the GpuArrayType class and use it when needed. Also the…

theano/sandbox/gpuarray/__init__.py

+import logging
+
 import theano
 from theano.configparser import config, AddConfigVar, StrParam, \
     BoolParam, IntParam
@@ -6,6 +8,8 @@ _logger_name = 'theano.sandbox.gpuarray'
 _logger = logging.getLogger(_logger_name)
 _logger.setLevel(logging.WARNING)
 
+error = _logger.error
+
 try:
     import pygpu.gpuarray
 except ImportError:
@@ -33,7 +37,7 @@ def init_dev(dev):
         devnum = int(dev[4:])
     elif dev.startswith('opencl'):
         globals.kind = 'opencl'
-        devspec = dev[6:]
+        devspec = dev[7:]
         plat, dev = devspec.split(':')
         devnum = int(dev)|(int(plat)>>16)
     else:
@@ -41,7 +45,6 @@ def init_dev(dev):
     if globals.kind:
         globals.context = pygpu.gpuarray.init(globals.kind, devnum)
 
-
 if pygpu:
     try:
         if (config.device.startswith('cuda') or
@@ -53,5 +56,5 @@ if pygpu:
         elif config.gpuarray.init_device != '':
             init_dev(config.gpuarray.init_device)
     except Exception:
-        print >> sys.stderr, "Could not initialize pygpu, support disabled"
+        error("Could not initialize pygpu, support disabled", exc_info=True)
         pygpu = None

theano/sandbox/gpuarray/basic_ops.py

@@ -7,8 +7,7 @@ from theano.scalar import Scalar
 
 from theano.gof.python25 import all, any
 
-from theano.sandbox.cuda.type import CudaNdArrayType
-
+import pygpu
 from pygpu import gpuarray, elemwise
 
 from type import GpuArrayType
@@ -21,8 +20,8 @@ def as_gpuarray_variable(x):
     return gpu_from_host(tensor_x)
 
 
-def as_gpuarray(x):
-    return gpuarray.array(x, copy=False)
+def as_gpuarray(x, kind, context):
+    return gpuarray.array(x, kind=kind, context=context, copy=False)
 
 
 class HostFromGpu(Op):
@@ -73,12 +72,13 @@ class GpuFromHost(Op):
         if not isinstance(x.type, tensor.TensorType):
             raise TypeError(x)
         return Apply(self, [x], [GpuArrayType(broadcastable=x.broadcastable,
-                                              dtype=x.dtype)]())
+                                              dtype=x.dtype)()])
 
     def perform(self, node, inp, out):
         x, = inp
         z, = out
-        z[0] = gpuarray.array(x)
+        type = node.outputs[0].type
+        z[0] = gpuarray.array(x, kind=type.kind, context=type.context)
 
     def grad(self, inputs, grads):
         gz, = grads
@@ -119,8 +119,8 @@ class GpuFromCuda(Op):
             while hasattr(base, 'base') and base.base is not None:
                 base = base.base
             raise NotImplementedError("How are we going to get a gpudata pointer from here")
-            x[0] = gpuarray.from_gpudata(b, 0, base=base, x.dtype,
-                                         x.shape, kind=globals.kind,
+            x[0] = gpuarray.from_gpudata(b, 0, x.dtype, x.shape,
+                                         base=base, kind=globals.kind,
                                          context=globals.context,
                                          strides=x.strides)
         else:

theano/sandbox/gpuarray/type.py

@@ -5,11 +5,10 @@ import numpy
 import theano
 from theano import Type, Variable, tensor, config, scalar
 
-import globals
-
 # Make sure this is importable even if pygpu is absent
 # (it will not work though)
 try:
+    import pygpu
     from pygpu import gpuarray
     from pygpu.elemwise import compare
 except ImportError:
@@ -22,12 +21,21 @@ class GpuArrayType(Type):
     
     @staticmethod
     def value_zeros(*args, **kwargs):
-        pygpu.gpuarray.zeros(*args, **kwargs, kind=globals.kind,
-                             context=globals.context)
+        pygpu.gpuarray.zeros(*args, kind=globals.kind,
+                              context=globals.context, **kwargs)
 
-    def __init__(self, dtype, broadcastable, name=None):
+    def __init__(self, dtype, broadcastable, kind=None, context=None,
+                 name=None):
+        import globals
+        if kind is None:
+            kind = globals.kind
+        if context is None:
+            context = globals.context
         self.dtype = str(dtype)
-        self.broadcastable = tuple(bool(b), for b in broadcastable)
+        self.broadcastable = tuple(bool(b) for b in broadcastable)
+        self.ndim = len(self.broadcastable)
+        self.kind = kind
+        self.context = context
         self.name = name
         try:
             self.typecode = gpuarray.dtype_to_typecode(self.dtype)
@@ -40,6 +48,10 @@ class GpuArrayType(Type):
             if not isinstance(data, gpuarray.GpuArray):
                 raise TypeError("%s expected a GpuArray object." % self,
                                 data, type(data))
+            if self.kind != data.kind:
+                raise TypeError("kind of GpuArray does not match")
+            if self.context != data.context:
+                raise TypeError("context of GpuArray differs")
             if self.typecode != data.typecode:
                 raise TypeError("%s expected typecode %d (dtype %s), "
                                 "got %d (dtype %s)." %
@@ -48,7 +60,7 @@ class GpuArrayType(Type):
             # fallthrough to ndim check
         elif allow_downcast:
             data = gpuarray.array(data, dtype=self.typecode, copy=False,
-                                  kind=globals.kind, context=globals.context,
+                                  kind=self.kind, context=self.context,
                                   ndmin=len(self.broadcastable))
         else:
             if isinstance(data, gpuarray.GpuArray):
@@ -65,12 +77,10 @@ class GpuArrayType(Type):
                             "got %s with shape %s." % (self.ndim, data.ndim,
                                                        data.shape), data)
         shp = data.shape
-        for b in self.broadcastable:
+        for i, b in enumerate(self.broadcastable):
             if b and shp[i] != 1:
                 raise TypeError("Non-unit value on shape on a broadcastable"
                                 " dimension.", shp, self.broadcastable)
-            i += 1
-
         return data
 
     def values_eq(self, a, b):
@@ -83,10 +93,13 @@ class GpuArrayType(Type):
     def __eq__(self, other):
         return (type(self) == type(other) and
                 self.typecode == other.typecode and
-                self.broadcastable == other.broadcastable)
+                self.broadcastable == other.broadcastable and
+                self.kind == other.kind and
+                self.context == other.context)
 
     def __hash__(self):
-        return hash(self.typecode) ^ hash(self.broadcastable)
+        return (hash(self.typecode) ^ hash(self.broadcastable) ^
+                hash(self.kind) ^ hash(self.context))
 
     def __str__(self):
         return "GpuArray<%s>" % self.dtype

theano/sandbox/gpuarray/var.py

@@ -10,7 +10,7 @@ try:
 except ImportError:
     pass
 
-from var import GpuArrayType
+from type import GpuArrayType
 from basic_ops import host_from_gpu, gpu_from_host
 
 class _operators(tensor.basic._tensor_py_operators):
@@ -61,7 +61,7 @@ class GpuArraySharedVariable(_operators, SharedVariable):
         self.container.value = pygpu.gpuarray.array(value, copy=(not borrow))
 
     def __getitem__(self, *args):
-        retrurn _operators.__getitem__(self, *args)
+        return _operators.__getitem__(self, *args)
 
 
 GpuArrayType.SharedVariable = GpuArraySharedVariable