
.. _using_gpu:

=============
Using the GPU
=============

For an introductory discussion of *Graphical Processing Units* (GPU)
and their use for intensive parallel computation purposes, see `GPGPU
<http://en.wikipedia.org/wiki/GPGPU>`_.

One of Theano's design goals is to specify computations at an abstract
level, so that the internal function compiler has a lot of flexibility
about how to carry out those computations.  One of the ways we take
advantage of this flexibility is in carrying out calculations on a
graphics card.

There are two ways currently to use a gpu, one that should support any OpenCL
device as well as NVIDIA cards (:ref:`gpuarray`), and the old backend that
only supports NVIDIA cards (:ref:`cuda`).

.. warning::

  If you want to use the new GpuArray backend, make sure to have the 
  development version of Theano installed. The 0.8.X releases have not
  been optimized to work correctly with the new backend.

.. _gpuarray:

GpuArray Backend
----------------

If you have not done so already, you will need to install libgpuarray
as well as at least one computing toolkit.  Instructions for doing so
are provided at `libgpuarray <http://deeplearning.net/software/libgpuarray/installation.html>`_.

While all types of devices are supported if using OpenCL, for the
remainder of this section, whatever compute device you are using will
be referred to as GPU.

.. warning::

  The backend was designed to support OpenCL, however current support is
  incomplete. A lot of very useful ops still do not support it because they
  were ported from the old backend with minimal change.

Testing Theano with GPU
~~~~~~~~~~~~~~~~~~~~~~~

To see if your GPU is being used, cut and paste the following program
into a file and run it.

Use the Theano flag ``device=cuda`` to require the use of the GPU. Use the flag
``device=cuda{0,1,...}`` to specify which GPU to use.

.. testcode::

  from theano import function, config, shared, tensor
  import numpy
  import time

  vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
  iters = 1000

  rng = numpy.random.RandomState(22)
  x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
  f = function([], tensor.exp(x))
  print(f.maker.fgraph.toposort())
  t0 = time.time()
  for i in range(iters):
      r = f()
  t1 = time.time()
  print("Looping %d times took %f seconds" % (iters, t1 - t0))
  print("Result is %s" % (r,))
  if numpy.any([isinstance(x.op, tensor.Elemwise) and
                ('Gpu' not in type(x.op).__name__)
                for x in f.maker.fgraph.toposort()]):
      print('Used the cpu')
  else:
      print('Used the gpu')

The program just computes ``exp()`` of a bunch of random numbers.  Note
that we use the :func:`theano.shared` function to make sure that the
input *x* is stored on the GPU.

.. testoutput::
   :hide:
   :options: +ELLIPSIS

   [Elemwise{exp,no_inplace}(<TensorType(float64, vector)>)]
   Looping 1000 times took ... seconds
   Result is ...
   Used the cpu

.. code-block:: none

  $ THEANO_FLAGS=device=cpu python gpu_tutorial1.py
  [Elemwise{exp,no_inplace}(<TensorType(float64, vector)>)]
  Looping 1000 times took 2.271284 seconds
  Result is [ 1.23178032  1.61879341  1.52278065 ...,  2.20771815  2.29967753
    1.62323285]
  Used the cpu

  $ THEANO_FLAGS=device=cuda0 python gpu_tutorial1.py
  Mapped name None to device cuda0: GeForce GTX 680 (cuDNN version 5004)
  [GpuElemwise{exp,no_inplace}(<GpuArrayType<None>(float64, (False,))>), HostFromGpu(gpuarray)(GpuElemwise{exp,no_inplace}.0)]
  Looping 1000 times took 1.202734 seconds
  Result is [ 1.23178032  1.61879341  1.52278065 ...,  2.20771815  2.29967753
    1.62323285]
  Used the gpu


Returning a Handle to Device-Allocated Data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

By default functions that execute on the GPU still return a standard
numpy ndarray.  A transfer operation is inserted just before the
results are returned to ensure a consistent interface with CPU code.
This allows changing the device some code runs on by only replacing
the value of the ``device`` flag without touching the code.

If you don't mind a loss of flexibility, you can ask theano to return
the GPU object directly.  The following code is modified to do just that.

.. testcode::

  from theano import function, config, shared, tensor
  import numpy
  import time

  vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
  iters = 1000

  rng = numpy.random.RandomState(22)
  x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
  f = function([], tensor.exp(x).transfer('dev0'))
  print(f.maker.fgraph.toposort())
  t0 = time.time()
  for i in range(iters):
      r = f()
  t1 = time.time()
  print("Looping %d times took %f seconds" % (iters, t1 - t0))
  print("Result is %s" % (numpy.asarray(r),))
  if numpy.any([isinstance(x.op, tensor.Elemwise) and
                ('Gpu' not in type(x.op).__name__)
                for x in f.maker.fgraph.toposort()]):
      print('Used the cpu')
  else:
      print('Used the gpu')

Here ``tensor.exp(x).transfer('None')`` means "copy ``exp(x)`` to the GPU",
with ``None`` the default GPU context when not explicitly given.
For information on how to set GPU contexts, see :ref:`tut_using_multi_gpu`. 

The output is

.. testoutput::
   :hide:
   :options: +ELLIPSIS, +SKIP

   Using device cuda0: ...
   [GpuElemwise{exp,no_inplace}(<GpuArray<float64>>)]
   Looping 1000 times took ... seconds
   Result is ...
   Used the gpu

.. code-block:: none

  $ THEANO_FLAGS=device=cuda0 python gpu_tutorial2.py
  Mapped name None to device cuda0: GeForce GTX 680 (cuDNN version 5004)
  [GpuElemwise{exp,no_inplace}(<GpuArrayType<None>(float64, (False,))>)]
  Looping 1000 times took 0.089194 seconds
  Result is [ 1.23178032  1.61879341  1.52278065 ...,  2.20771815  2.29967753
    1.62323285]
  Used the gpu

While the time per call appears to be much lower than the two previous
invocations (and should indeed be lower, since we avoid a transfer)
the massive speedup we obtained is in part due to asynchronous nature
of execution on GPUs, meaning that the work isn't completed yet, just
'launched'.  We'll talk about that later.

The object returned is a GpuArray from pygpu.  It mostly acts as a
numpy ndarray with some exceptions due to its data being on the GPU.
You can copy it to the host and convert it to a regular ndarray by
using usual numpy casting such as ``numpy.asarray()``.

For even more speed, you can play with the ``borrow`` flag.  See
:ref:`borrowfunction`.

What Can be Accelerated on the GPU
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The performance characteristics will of course vary from device to
device, and also as we refine our implementation:

* In general, matrix multiplication, convolution, and large element-wise
  operations can be accelerated a lot (5-50x) when arguments are large enough
  to keep 30 processors busy.
* Indexing, dimension-shuffling and constant-time reshaping will be equally fast
  on GPU as on CPU.
* Summation over rows/columns of tensors can be a little slower on the
  GPU than on the CPU.
* Copying of large quantities of data to and from a device is relatively slow,
  and often cancels most of the advantage of one or two accelerated functions
  on that data. Getting GPU performance largely hinges on making data transfer
  to the device pay off.

The backend supports all regular theano data types (float32, float64,
int, ...), however GPU support varies and some units can't deal with
double (float64) or small (less than 32 bits like int16) data types.
You will get an error at compile time or runtime if this is the case.

By default all inputs will get transferred to GPU. You can prevent an
input from getting transferred by setting its tag.target attribute to
'cpu'.

Complex support is untested and most likely completely broken.

Tips for Improving Performance on GPU
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

* Consider adding ``floatX=float32`` (or the type you are using) to your
  ``.theanorc`` file if you plan to do a lot of GPU work.
* The GPU backend supports *float64* variables, but they are still slower
  to compute than *float32*. The more *float32*, the better GPU performance
  you will get. 
* Prefer constructors like ``matrix``, ``vector`` and ``scalar`` to
  ``dmatrix``, ``dvector`` and ``dscalar`` because the former will give
  you *float32* variables and ignore the type given to ``floatX``.
* Minimize transfers to the GPU device by using ``shared`` variables
  to store frequently-accessed data (see :func:`shared()<shared.shared>`).
  When using the GPU, tensor ``shared`` variables are stored on
  the GPU by default to eliminate transfer time for GPU ops using those variables.
* If you aren't happy with the performance you see, try running your script with
  ``profile=True`` flag. This should print some timing information at program
  termination. Is time being used sensibly?   If an op or Apply is
  taking more time than its share, then if you know something about GPU
  programming, have a look at how it's implemented in theano.gpuarray.
  Check the line similar to *Spent Xs(X%) in cpu op, Xs(X%) in gpu op and
  Xs(X%) in transfer op*. This can tell you if not enough of your graph is
  on the GPU or if there is too much memory transfer.
* To investigate whether all the Ops in the computational graph are
  running on GPU, it is possible to debug or check your code by providing
  a value to `assert_no_cpu_op` flag, i.e. `warn`, for warning, `raise` for
  raising an error or `pdb` for putting a breakpoint in the computational
  graph if there is a CPU Op.

  .. _gpu_async:

GPU Async Capabilities
~~~~~~~~~~~~~~~~~~~~~~

By default, all operations on the GPU are run asynchronously.  This
means that they are only scheduled to run and the function returns.
This is made somewhat transparently by the underlying libgpuarray.

A forced synchronization point is introduced when doing memory
transfers between device and host.

It is possible to force synchronization for a particular GpuArray by
calling its ``sync()`` method.  This is useful to get accurate timings
when doing benchmarks.


Changing the Value of Shared Variables
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To change the value of a ``shared`` variable, e.g. to provide new data to processes,
use ``shared_variable.set_value(new_value)``. For a lot more detail about this,
see :ref:`aliasing`.

Exercise
~~~~~~~~

Consider again the logistic regression:

.. testcode::

    import numpy
    import theano
    import theano.tensor as T
    rng = numpy.random

    N = 400
    feats = 784
    D = (rng.randn(N, feats).astype(theano.config.floatX),
    rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))
    training_steps = 10000

    # Declare Theano symbolic variables
    x = T.matrix("x")
    y = T.vector("y")
    w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
    b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
    x.tag.test_value = D[0]
    y.tag.test_value = D[1]

    # Construct Theano expression graph
    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
    prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
    xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
    cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
    gw,gb = T.grad(cost, [w,b])

    # Compile expressions to functions
    train = theano.function(
                inputs=[x,y],
                outputs=[prediction, xent],
                updates=[(w, w-0.01*gw), (b, b-0.01*gb)],
                name = "train")
    predict = theano.function(inputs=[x], outputs=prediction,
                name = "predict")

    if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
            train.maker.fgraph.toposort()]):
        print('Used the cpu')
    elif any([x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in
              train.maker.fgraph.toposort()]):
        print('Used the gpu')
    else:
        print('ERROR, not able to tell if theano used the cpu or the gpu')
        print(train.maker.fgraph.toposort())

    for i in range(training_steps):
        pred, err = train(D[0], D[1])

    print("target values for D")
    print(D[1])

    print("prediction on D")
    print(predict(D[0]))

.. testoutput::
   :hide:
   :options: + ELLIPSIS

   Used the cpu
   target values for D
   ...
   prediction on D
   ...

Modify and execute this example to run on GPU with ``floatX=float32`` and
time it using the command line ``time python file.py``. (Of course, you may use some of your answer
to the exercise in section :ref:`Configuration Settings and Compiling Mode<using_modes>`.)

Is there an increase in speed from CPU to GPU?

Where does it come from? (Use ``profile=True`` flag.)

What can be done to further increase the speed of the GPU version? Put your ideas to test.

:download:`Solution<using_gpu_solution_1.py>`

-------------------------------------------

.. _cuda:

CUDA backend
------------

If you have not done so already, you will need to install Nvidia's
GPU-programming toolchain (CUDA) and configure Theano to use it.
We provide installation instructions for :ref:`Linux <gpu_linux>`,
:ref:`MacOS <gpu_macos>` and :ref:`Windows <gpu_windows>`.

The old CUDA backend can be activated using the flags ``device=gpu`` or
``device=gpu{0,1,...}``

.. Note::

   * Only 32 bit floats are supported.
   * ``Shared`` variables with *float32* dtype are by default moved to the GPU memory space.

   * There is a limit of one GPU per process.

   * Apply the Theano flag ``floatX=float32`` (through ``theano.config.floatX``) in your code.
   * ``Cast`` inputs before storing them into a ``shared`` variable.
   * Circumvent the automatic cast of *int32* with *float32* to *float64*:

     * Insert manual cast in your code or use *[u]int{8,16}*.
     * Insert manual cast around the mean operator (this involves division by length, which is an *int64*).
     * Notice that a new casting mechanism is being developed.

-------------------------------------------


Software for Directly Programming a GPU
---------------------------------------

Leaving aside Theano which is a meta-programmer, there are:

* **CUDA**: GPU programming API by NVIDIA based on extension to C (CUDA C)

  * Vendor-specific

  * Numeric libraries (BLAS, RNG, FFT) are maturing.

* **OpenCL**: multi-vendor version of CUDA

  * More general, standardized.

  * Fewer libraries, lesser spread.

* **PyCUDA**: Python bindings to CUDA driver interface allow to access Nvidia's CUDA parallel
  computation API from Python

  * Convenience:

    Makes it easy to do GPU meta-programming from within Python.

    Abstractions to compile low-level CUDA code from Python (``pycuda.driver.SourceModule``).

    GPU memory buffer (``pycuda.gpuarray.GPUArray``).

    Helpful documentation.

  * Completeness: Binding to all of CUDA's driver API.

  * Automatic error checking: All CUDA errors are automatically translated into Python exceptions.

  * Speed: PyCUDA's base layer is written in C++.

  * Good memory management of GPU objects:

    Object cleanup tied to lifetime of objects (RAII, 'Resource Acquisition Is Initialization').

    Makes it much easier to write correct, leak- and crash-free code.

    PyCUDA knows about dependencies (e.g. it won't detach from a context before all memory
    allocated in it is also freed).


  (This is adapted from PyCUDA's `documentation <http://documen.tician.de/pycuda/index.html>`_
  and Andreas Kloeckner's `website <http://mathema.tician.de/software/pycuda>`_ on PyCUDA.)


* **PyOpenCL**: PyCUDA for OpenCL

Learning to Program with PyCUDA
-------------------------------

If you already enjoy a good proficiency with the C programming language, you
may easily leverage your knowledge by learning, first, to program a GPU with the
CUDA extension to C (CUDA C) and, second, to use PyCUDA to access the CUDA
API with a Python wrapper.

The following resources will assist you in this learning process:

* **CUDA API and CUDA C: Introductory**

  * `NVIDIA's slides <http://www.sdsc.edu/us/training/assets/docs/NVIDIA-02-BasicsOfCUDA.pdf>`_

  * `Stein's (NYU) slides <http://www.cs.nyu.edu/manycores/cuda_many_cores.pdf>`_

* **CUDA API and CUDA C: Advanced**

  * `MIT IAP2009 CUDA <https://sites.google.com/site/cudaiap2009/home>`_
    (full coverage: lectures, leading Kirk-Hwu textbook, examples, additional resources)

  * `Course U. of Illinois <http://courses.engr.illinois.edu/ece498/al/index.html>`_
    (full lectures, Kirk-Hwu textbook)

  * `NVIDIA's knowledge base <http://www.nvidia.com/content/cuda/cuda-developer-resources.html>`_
    (extensive coverage, levels from introductory to advanced)

  * `practical issues <http://stackoverflow.com/questions/2392250/understanding-cuda-grid-dimensions-block-dimensions-and-threads-organization-s>`_
    (on the relationship between grids, blocks and threads; see also linked and related issues on same page)

  * `CUDA optimisation <http://www.gris.informatik.tu-darmstadt.de/cuda-workshop/slides.html>`_

* **PyCUDA: Introductory**

  * `Kloeckner's slides <http://www.gputechconf.com/gtcnew/on-demand-gtc.php?sessionTopic=&searchByKeyword=kloeckner&submit=&select=+&sessionEvent=2&sessionYear=2010&sessionFormat=3>`_

  * `Kloeckner' website <http://mathema.tician.de/software/pycuda>`_

* **PYCUDA: Advanced**

  * `PyCUDA documentation website <http://documen.tician.de/pycuda/>`_


The following examples give a foretaste of programming a GPU with PyCUDA. Once
you feel competent enough, you may try yourself on the corresponding exercises.

**Example: PyCUDA**


.. code-block:: python

  # (from PyCUDA's documentation)
  import pycuda.autoinit
  import pycuda.driver as drv
  import numpy

  from pycuda.compiler import SourceModule
  mod = SourceModule("""
  __global__ void multiply_them(float *dest, float *a, float *b)
  {
    const int i = threadIdx.x;
    dest[i] = a[i] * b[i];
  }
  """)

  multiply_them = mod.get_function("multiply_them")

  a = numpy.random.randn(400).astype(numpy.float32)
  b = numpy.random.randn(400).astype(numpy.float32)

  dest = numpy.zeros_like(a)
  multiply_them(
          drv.Out(dest), drv.In(a), drv.In(b),
          block=(400,1,1), grid=(1,1))

  assert numpy.allclose(dest, a*b)
  print(dest)


Exercise
~~~~~~~~

Run the preceding example.

Modify and execute to work for a matrix of shape (20, 10).



.. _pyCUDA_theano:

**Example: Theano + PyCUDA**


.. code-block:: python

    import numpy, theano
    import theano.misc.pycuda_init
    from pycuda.compiler import SourceModule
    import theano.sandbox.cuda as cuda

    class PyCUDADoubleOp(theano.Op):

        __props__ = ()

        def make_node(self, inp):
            inp = cuda.basic_ops.gpu_contiguous(
               cuda.basic_ops.as_cuda_ndarray_variable(inp))
            assert inp.dtype == "float32"
            return theano.Apply(self, [inp], [inp.type()])

        def make_thunk(self, node, storage_map, _, _2):
            mod = SourceModule("""
        __global__ void my_fct(float * i0, float * o0, int size) {
        int i = blockIdx.x*blockDim.x + threadIdx.x;
        if(i<size){
            o0[i] = i0[i]*2;
        }
      }""")
            pycuda_fct = mod.get_function("my_fct")
            inputs = [storage_map[v] for v in node.inputs]
            outputs = [storage_map[v] for v in node.outputs]

            def thunk():
                z = outputs[0]
                if z[0] is None or z[0].shape != inputs[0][0].shape:
                    z[0] = cuda.CudaNdarray.zeros(inputs[0][0].shape)
                grid = (int(numpy.ceil(inputs[0][0].size / 512.)), 1)
                pycuda_fct(inputs[0][0], z[0], numpy.intc(inputs[0][0].size),
                           block=(512, 1, 1), grid=grid)
            return thunk


Use this code to test it:

>>> x = theano.tensor.fmatrix()
>>> f = theano.function([x], PyCUDADoubleOp()(x))  # doctest: +SKIP
>>> xv = numpy.ones((4, 5), dtype="float32")
>>> assert numpy.allclose(f(xv), xv*2)  # doctest: +SKIP
>>> print(numpy.asarray(f(xv)))  # doctest: +SKIP


Exercise
~~~~~~~~

Run the preceding example.

Modify and execute to multiply two matrices: *x* * *y*.

Modify and execute to return two outputs: *x + y* and *x - y*.

(Notice that Theano's current *elemwise fusion* optimization is
only applicable to computations involving a single output. Hence, to gain
efficiency over the basic solution that is asked here, the two operations would
have to be jointly optimized explicitly in the code.)

Modify and execute to support *stride* (i.e. to avoid constraining the input to be *C-contiguous*).

Note
----

* See :ref:`example_other_random` to know how to handle random numbers
  on the GPU.

* The mode `FAST_COMPILE` disables C code, so also disables the GPU. You
  can use the Theano flag optimizer='fast_compile' to speed up
  compilation and keep the GPU.
