pep8

theano/misc/pycuda_example.py

-"""
-This file show how we can use Pycuda compiled fct in a Theano Op. Do no use those op in production code. See the TODO.
+"""This file show how we can use Pycuda compiled fct in a Theano
+Op. Do no use those op in production code. See the TODO.
 
 You can use them as a guide to use your pycuda code into a Theano op.
 
-The PycudaElemwiseSourceModuleOp is a Theano op use pycuda code generated with pycuda.compiler.SourceModule
+The PycudaElemwiseSourceModuleOp is a Theano op use pycuda code
+generated with pycuda.compiler.SourceModule
 
-The PycudaElemwiseKernelOp op use pycuda code generated with pycuda.elementwise.ElementwiseKernel. It must be wrapper by TheanoElementwiseKernel.
+The PycudaElemwiseKernelOp op use pycuda code generated with
+pycuda.elementwise.ElementwiseKernel. It must be wrapper by
+TheanoElementwiseKernel.
 
 Their is a test in test_pycuda.py.
 
-This don't work with broadcast and non-contiguous memory as pycuda don't support that, but we make sure we don't introduce problem.
+This don't work with broadcast and non-contiguous memory as pycuda
+don't support that, but we make sure we don't introduce problem.
   If the memory is non-contiguous, we create a new copy that is contiguous.
   If their is broadcasted dimensions, we raise an error.
+
 """
 
 import numpy
@@ -19,7 +24,8 @@ import numpy
 import theano
 from theano.gof import Op, Apply, local_optimizer, EquilibriumDB
 from theano.sandbox.cuda import GpuElemwise, CudaNdarrayType, GpuOp
-from theano.sandbox.cuda.basic_ops import as_cuda_ndarray_variable, gpu_contiguous
+from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
+                                           gpu_contiguous)
 from theano.sandbox.cuda.opt import gpu_seqopt
 
 import pycuda_init
@@ -30,30 +36,36 @@ import pycuda
 from pycuda.elementwise import ElementwiseKernel
 from pycuda.compiler import SourceModule
 from pycuda.tools import VectorArg
+import pycuda.gpuarray
+
 
 def theano_parse_c_arg(c_arg):
-    c_arg = c_arg.replace('npy_float32','float')
-    c_arg = c_arg.replace('npy_float64','double')
-    c_arg = c_arg.replace('npy_int32','int')
-    c_arg = c_arg.replace('npy_int8','char')
-    c_arg = c_arg.replace('npy_ucs4','unsigned int')
-    c_arg = c_arg.replace('npy_uint32','unsigned int')
-    c_arg = c_arg.replace('npy_uint16','unsigned short')
-    c_arg = c_arg.replace('npy_uint8','unsigned char')
+    c_arg = c_arg.replace('npy_float32', 'float')
+    c_arg = c_arg.replace('npy_float64', 'double')
+    c_arg = c_arg.replace('npy_int32', 'int')
+    c_arg = c_arg.replace('npy_int8', 'char')
+    c_arg = c_arg.replace('npy_ucs4', 'unsigned int')
+    c_arg = c_arg.replace('npy_uint32', 'unsigned int')
+    c_arg = c_arg.replace('npy_uint16', 'unsigned short')
+    c_arg = c_arg.replace('npy_uint8', 'unsigned char')
     return pycuda.tools.parse_c_arg(c_arg)
 
+
 class TheanoElementwiseKernel(pycuda.elementwise.ElementwiseKernel):
     def __init__(self, arguments, operation,
                  name="kernel", keep=False, options=[], **kwargs):
         if isinstance(arguments, basestring):
-            arguments = [theano_parse_c_arg(arg) for arg in arguments.split(",")]
-            pycuda.elementwise.ElementwiseKernel.__init__(self, arguments, operation, name, keep, options, **kwargs)
+            arguments = [theano_parse_c_arg(arg)
+                         for arg in arguments.split(",")]
+        pycuda.elementwise.ElementwiseKernel.__init__(self, arguments,
+                                                      operation, name, keep,
+                                                      options, **kwargs)
 
     def __call__(self, *args):
         vectors = []
 
         invocation_args = []
-        for arg, arg_descr in zip(args, self.arguments):
+        for arg, arg_descr in zip(args, self.gen_kwargs["arguments"]):
             if isinstance(arg_descr, VectorArg):
                 vectors.append(arg)
                 invocation_args.append(arg.gpudata)
@@ -62,7 +74,7 @@ class TheanoElementwiseKernel(pycuda.elementwise.ElementwiseKernel):
 
         repr_vec = vectors[0]
         invocation_args.append(repr_vec.mem_size)
-        if hasattr(repr_vec,"_block") and hasattr(repr_vec,"_grid"):
+        if hasattr(repr_vec, "_block") and hasattr(repr_vec, "_grid"):
             self.func.set_block_shape(*repr_vec._block)
             self.func.prepared_call(repr_vec._grid, *invocation_args)
         else:
@@ -75,19 +87,20 @@ class PycudaElemwiseSourceModuleOp(GpuOp):
     nin = property(lambda self: self.scalar_op.nin)
     nout = property(lambda self: self.scalar_op.nout)
 
-    def __init__(self, scalar_op, inplace_pattern = {}, name = None):
+    def __init__(self, scalar_op, inplace_pattern={}, name=None):
         self.name = name
         self.scalar_op = scalar_op
-        self.inplace_pattern=None
+        self.inplace_pattern = None
 
     def __str__(self):
         if self.name is None:
             if self.inplace_pattern:
                 items = self.inplace_pattern.items()
                 items.sort()
-                return self.__class__.__name__+"{%s}%s" % (self.scalar_op, str(items))
+                return self.__class__.__name__ + "{%s}%s" % (self.scalar_op,
+                                                             str(items))
             else:
-                return self.__class__.__name__+"{%s}" % (self.scalar_op)
+                return self.__class__.__name__ + "{%s}" % (self.scalar_op)
         else:
             return self.name
 
@@ -101,17 +114,23 @@ class PycudaElemwiseSourceModuleOp(GpuOp):
 
         if any([any(i.type.broadcastable) for i in inputs]):
             raise Exception("pycuda don't support broadcasted dimensions")
-        assert len(inputs)==2#TODO remove
+        assert len(inputs) == 2  # TODO remove
 
-        otype = CudaNdarrayType(broadcastable=[False]*_inputs[0].type.ndim)
+        otype = CudaNdarrayType(broadcastable=[False] * _inputs[0].type.ndim)
         assert self.nout == 1
 
-        fct_name = "pycuda_elemwise_%s"%str(self.scalar_op)
+        fct_name = "pycuda_elemwise_%s" % str(self.scalar_op)
         out_node = Apply(self, _inputs, [otype() for o in xrange(self.nout)])
-        in_name = ["i"+str(id) for id in range(len(inputs))]
-        out_name = ["o"+str(id) for id in range(self.nout)]
-        c_code = self.scalar_op.c_code(out_node, "some_name", tuple([n+"[i]"for n in in_name]), tuple(n+"[i]"for n in out_name), {})
-        c_code_param = ", ".join([var.type.dtype_specs()[1]+" *"+name for var,name in zip(inputs,in_name) + zip(out_node.outputs,out_name)]+["int size"])
+        in_name = ["i" + str(id) for id in range(len(inputs))]
+        out_name = ["o" + str(id) for id in range(self.nout)]
+        c_code = self.scalar_op.c_code(out_node, "some_name",
+                                       tuple([n + "[i]" for n in in_name]),
+                                       tuple(n + "[i]" for n in out_name), {})
+        c_code_param = ", ".join([var.type.dtype_specs()[1] + " *" + name
+                                  for var, name in (zip(inputs, in_name) +
+                                                    zip(out_node.outputs,
+                                                        out_name))] +
+                                 ["int size"])
         mod = SourceModule("""
 #include<Python.h>
 #include <numpy/arrayobject.h>
@@ -123,7 +142,7 @@ class PycudaElemwiseSourceModuleOp(GpuOp):
         %s
     }
   }
-  """%(fct_name,c_code_param,c_code))
+  """ % (fct_name, c_code_param, c_code))
         self.pycuda_fct = mod.get_function(fct_name)
         return out_node
 
@@ -131,37 +150,40 @@ class PycudaElemwiseSourceModuleOp(GpuOp):
         #TODO support broadcast!
         #TODO assert all input have the same shape
         z, = out
-        if z[0] is None or z[0].shape!=inputs[0].shape:
+        if z[0] is None or z[0].shape != inputs[0].shape:
             z[0] = theano.sandbox.cuda.CudaNdarray.zeros(inputs[0].shape)
         if inputs[0].shape != inputs[1].shape:
-            raise TypeError("PycudaElemwiseSourceModuleOp: inputs don't have the same shape!")
+            raise TypeError("PycudaElemwiseSourceModuleOp:"
+                            " inputs don't have the same shape!")
 
         if inputs[0].size > 512:
-            grid = (int(numpy.ceil(inputs[0].size / 512.)),1)
-            block = (512,1,1)
+            grid = (int(numpy.ceil(inputs[0].size / 512.)), 1)
+            block = (512, 1, 1)
         else:
-            grid = (1,1)
-            block = (inputs[0].shape[0],inputs[0].shape[1],1)
-        self.pycuda_fct(inputs[0], inputs[1], z[0], numpy.intc(inputs[1].size), block=block, grid=grid)
+            grid = (1, 1)
+            block = (inputs[0].shape[0], inputs[0].shape[1], 1)
+        self.pycuda_fct(inputs[0], inputs[1], z[0],
+                        numpy.intc(inputs[1].size), block=block, grid=grid)
 
 
 class PycudaElemwiseKernelOp(GpuOp):
     nin = property(lambda self: self.scalar_op.nin)
     nout = property(lambda self: self.scalar_op.nout)
 
-    def __init__(self, scalar_op, inplace_pattern = {}, name = None):
+    def __init__(self, scalar_op, inplace_pattern={}, name=None):
         self.name = name
         self.scalar_op = scalar_op
-        self.inplace_pattern=None
+        self.inplace_pattern = None
 
     def __str__(self):
         if self.name is None:
             if self.inplace_pattern:
                 items = self.inplace_pattern.items()
                 items.sort()
-                return self.__class__.__name__+"{%s}%s" % (self.scalar_op, str(items))
+                return self.__class__.__name__ + "{%s}%s" % (self.scalar_op,
+                                                             str(items))
             else:
-                return self.__class__.__name__+"{%s}" % (self.scalar_op)
+                return self.__class__.__name__ + "{%s}" % (self.scalar_op)
         else:
             return self.name
 
@@ -175,9 +197,10 @@ class PycudaElemwiseKernelOp(GpuOp):
 
         if any([any(i.type.broadcastable) for i in inputs]):
             raise Exception("pycuda don't support broadcasted dimensions")
-        assert len(inputs)==2#TODO remove
+        assert len(inputs) == 2  # TODO remove
 
-# output is broadcastable only along dimensions where all inputs are broadcastable
+        # output is broadcastable only along dimensions where all inputs are
+        # broadcastable
         broadcastable = []
         for d in xrange(_inputs[0].type.ndim):
             bcast_d = True
@@ -192,14 +215,18 @@ class PycudaElemwiseKernelOp(GpuOp):
         assert self.nout == 1
 
         out_node = Apply(self, _inputs, [otype() for o in xrange(self.nout)])
-        in_name = ["i"+str(id) for id in range(len(inputs))]
-        out_name = ["o"+str(id) for id in range(self.nout)]
-        c_code = self.scalar_op.c_code(out_node, "some_name", tuple([n+"[i]"for n in in_name]), tuple(n+"[i]"for n in out_name), {})
+        in_name = ["i" + str(id) for id in range(len(inputs))]
+        out_name = ["o" + str(id) for id in range(self.nout)]
+        c_code = self.scalar_op.c_code(out_node, "some_name",
+                                       tuple([n + "[i]"for n in in_name]),
+                                       tuple(n + "[i]"for n in out_name), {})
 
         self.pycuda_fct = TheanoElementwiseKernel(
-            ", ".join([var.type.dtype_specs()[1]+" *"+name for var,name in zip(inputs,in_name) + zip(out_node.outputs,out_name)]),
+            ", ".join([var.type.dtype_specs()[1] + " *" + name
+                       for var, name in (zip(inputs, in_name) +
+                                         zip(out_node.outputs, out_name))]),
             c_code,
-            "pycuda_elemwise_kernel_%s"%str(self.scalar_op),
+            "pycuda_elemwise_kernel_%s" % str(self.scalar_op),
             preamble="""#include<Python.h>
 #include <numpy/arrayobject.h>""")
         return out_node
@@ -207,7 +234,7 @@ class PycudaElemwiseKernelOp(GpuOp):
     def perform(self, node, inputs, out):
         #TODO assert all input have the same shape
         z, = out
-        if z[0] is None or z[0].shape!=inputs[0].shape:
+        if z[0] is None or z[0].shape != inputs[0].shape:
             z[0] = theano.sandbox.cuda.CudaNdarray.zeros(inputs[0].shape)
         i = inputs + z
         self.pycuda_fct(*i)
@@ -215,17 +242,23 @@ class PycudaElemwiseKernelOp(GpuOp):
 pycuda_optimizer = EquilibriumDB()
 gpu_seqopt.register("pycuda_optimizer", pycuda_optimizer, 1.5, "fast_run")
 
+
 @local_optimizer([])
 def local_pycuda_gpu_elemwise(node):
     """
        GpuElemwise -> PycudaElemwiseSourceModuleOp
     """
     if isinstance(node.op, GpuElemwise):
-        if not any([ any(i.type.broadcastable) for i in node.inputs]) and all([i.ndim<=2 for i in node.inputs]):
-            new_op = PycudaElemwiseSourceModuleOp(node.op.scalar_op, node.op.inplace_pattern)(*node.inputs)
+        if (not any([any(i.type.broadcastable) for i in node.inputs]) and
+            all([i.ndim <= 2 for i in node.inputs])):
+            new_op = PycudaElemwiseSourceModuleOp(node.op.scalar_op,
+                                                  node.op.inplace_pattern)(
+                                                      *node.inputs)
             return [new_op]
 
-pycuda_optimizer.register("local_pycuda_gpu_elemwise", local_pycuda_gpu_elemwise)
+pycuda_optimizer.register("local_pycuda_gpu_elemwise",
+                          local_pycuda_gpu_elemwise)
+
 
 @local_optimizer([])
 def local_pycuda_gpu_elemwise_kernel(node):
@@ -233,8 +266,11 @@ def local_pycuda_gpu_elemwise_kernel(node):
        GpuElemwise -> PycudaElemwiseKernelOp
     """
     if isinstance(node.op, GpuElemwise):
-        if not any([ any(i.type.broadcastable) for i in node.inputs]):
-            new_op = PycudaElemwiseKernelOp(node.op.scalar_op, node.op.inplace_pattern)(*node.inputs)
+        if not any([any(i.type.broadcastable) for i in node.inputs]):
+            new_op = PycudaElemwiseKernelOp(node.op.scalar_op,
+                                            node.op.inplace_pattern)(
+                                                *node.inputs)
             return [new_op]
 
-pycuda_optimizer.register("local_pycuda_gpu_elemwise_kernel", local_pycuda_gpu_elemwise_kernel, 1.5)
+pycuda_optimizer.register("local_pycuda_gpu_elemwise_kernel",
+                          local_pycuda_gpu_elemwise_kernel, 1.5)