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Using the GPU

For an introductory discussion of Graphical Processing Units (GPU) and their use for intensive parallel computation purposes, see 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 an Nvidia graphics card when the device present in the computer is CUDA-enabled.

Setting Up CUDA

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 Linux, MacOS and Windows.

Testing Theano with GPU

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

from theano import function, config, shared, sandbox
import theano.tensor as T
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([], T.exp(x))
print f.maker.fgraph.toposort()
t0 = time.time()
for i in xrange(iters):
    r = f()
t1 = time.time()
print 'Looping %d times took' % iters, t1 - t0, 'seconds'
print 'Result is', r
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
    print 'Used the cpu'
else:
    print 'Used the gpu'

The program just computes the exp() of a bunch of random numbers. Note that we use the shared function to make sure that the input x is stored on the graphics device.

If I run this program (in check1.py) with device=cpu, my computer takes a little over 3 seconds, whereas on the GPU it takes just over 0.64 seconds. The GPU will not always produce the exact same floating-point numbers as the CPU. As a benchmark, a loop that calls numpy.exp(x.get_value()) takes about 46 seconds.

$ THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python check1.py
[Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)]
Looping 1000 times took 3.06635117531 seconds
Result is [ 1.23178029  1.61879337  1.52278066 ...,  2.20771813  2.29967761
  1.62323284]
Used the cpu

$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python check1.py
Using gpu device 0: GeForce GTX 580
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>), HostFromGpu(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.638810873032 seconds
Result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu

Note that GPU operations in Theano require for now floatX to be float32 (see also below).

Returning a Handle to Device-Allocated Data

The speedup is not greater in the preceding example because the function is returning its result as a NumPy ndarray which has already been copied from the device to the host for your convenience. This is what makes it so easy to swap in device=gpu, but if you don’t mind less portability, you might gain a bigger speedup by changing the graph to express a computation with a GPU-stored result. The gpu_from_host op means “copy the input from the host to the GPU” and it is optimized away after the T.exp(x) is replaced by a GPU version of exp().

from theano import function, config, shared, sandbox
import theano.tensor as T
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([], sandbox.cuda.basic_ops.gpu_from_host(T.exp(x)))
print f.maker.fgraph.toposort()
t0 = time.time()
for i in xrange(iters):
    r = f()
t1 = time.time()
print 'Looping %d times took' % iters, t1 - t0, 'seconds'
print 'Result is', r
print 'Numpy result is', numpy.asarray(r)
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
    print 'Used the cpu'
else:
    print 'Used the gpu'

The output from this program is

$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python check2.py
Using gpu device 0: GeForce GTX 580
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>)]
Looping 1000 times took 0.34898686409 seconds
Result is <CudaNdarray object at 0x6a7a5f0>
Numpy result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu

Here we’ve shaved off about 50% of the run-time by simply not copying the resulting array back to the host. The object returned by each function call is now not a NumPy array but a “CudaNdarray” which can be converted to a NumPy ndarray by the normal NumPy casting mechanism.

Running the GPU at Full Speed

To really get maximum performance in this simple example, we need to use an out instance with the flag borrow=True to tell Theano not to copy the output it returns to us. This is because Theano pre-allocates memory for internal use (like working buffers), and by default will never return a result that is aliased to one of its internal buffers: instead, it will copy the buffers associated to outputs into newly allocated memory at each function call. This is to ensure that subsequent function calls will not overwrite previously computed outputs. Although this is normally what you want, our last example was so simple that it had the unwanted side-effect of really slowing things down.

from theano import function, config, shared, sandbox, Out
import theano.tensor as T
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([],
        Out(sandbox.cuda.basic_ops.gpu_from_host(T.exp(x)),
            borrow=True))
print f.maker.fgraph.toposort()
t0 = time.time()
for i in xrange(iters):
    r = f()
t1 = time.time()
print 'Looping %d times took' % iters, t1 - t0, 'seconds'
print 'Result is', r
print 'Numpy result is', numpy.asarray(r)
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
    print 'Used the cpu'
else:
    print 'Used the gpu'

Running this version of the code takes just over 0.05 seconds, that is 60x faster than the CPU implementation!

With *flag* ``borrow=False``:

$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python using_gpu_solution_1.py
Using gpu device 0: GeForce GTX 580
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>)]
Looping 1000 times took 0.31614613533 seconds
Result is <CudaNdarray object at 0x77e9270>
Numpy result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu

With *flag* ``borrow=True``:

$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python using_gpu_solution_1.py
Using gpu device 0: GeForce GTX 580
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>)]
Looping 1000 times took 0.0502779483795 seconds
Result is <CudaNdarray object at 0x83e5cb0>
Numpy result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu

This version of the code including the flag borrow=True is slightly less safe because if we had saved the r returned from one function call, we would have to take care and remember that its value might be over-written by a subsequent function call. Although borrow=True makes a dramatic difference in this example, be careful! The advantage of borrow=True is much weaker in larger graphs, and there is a lot of potential for making a mistake by failing to account for the resulting memory aliasing.

What Can Be Accelerated on the GPU

The performance characteristics will change as we continue to optimize our implementations, and vary from device to device, but to give a rough idea of what to expect right now:

  • Only computations with float32 data-type can be accelerated. Better support for float64 is expected in upcoming hardware but float64 computations are still relatively slow (Jan 2010).
  • 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.

Tips for Improving Performance on GPU

  • Consider adding floatX=float32 to your .theanorc file if you plan to do a lot of GPU work.
  • Use the Theano flag allow_gc=False. See GPU Async capabilities
  • Prefer constructors like matrix, vector and scalar to dmatrix, dvector and dscalar because the former will give you float32 variables when floatX=float32.
  • Ensure that your output variables have a float32 dtype and not float64. The more float32 variables are in your graph, the more work the GPU can do for you.
  • Minimize tranfers to the GPU device by using shared float32 variables to store frequently-accessed data (see shared()). When using the GPU, float32 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 building your functions with mode='ProfileMode'. 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.sandbox.cuda. 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.
  • Use nvcc options. nvcc supports those options to speed up some computations: -ftz=true to flush denormals values to zeros., –prec-div=false and –prec-sqrt=false options to speed up division and square root operation by being less precise. You can enable all of them with the nvcc.flags=–use_fast_math Theano flag or you can enable them individually as in this example: nvcc.flags=-ftz=true –prec-div=false.

GPU Async capabilities

Ever since Theano 0.6 we started to use the asynchronous capability of GPUs. This allows us to be faster but with the possibility that some errors may be raised later than when they should occur. This can cause difficulties when profiling Theano apply nodes. There is a NVIDIA driver feature to help with these issues. If you set the environment variable CUDA_LAUNCH_BLOCKING=1 then all kernel calls will be automatically synchronized. This reduces performance but provides good profiling and appropriately placed error messages.

This feature interacts with Theano garbage collection of intermediate results. To get the most of this feature, you need to disable the gc as it inserts synchronization points in the graph. Set the Theano flag allow_gc=False to get even faster speed! This will raise the memory usage.

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 Understanding Memory Aliasing for Speed and Correctness.

Exercise

Consider again the logistic regression:

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]
#print "Initial model:"
#print w.get_value(), b.get_value()

# 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 "Final model:"
#print w.get_value(), b.get_value()

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

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

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 Configuration Settings and Compiling Mode.)

Is there an increase in speed from CPU to GPU?

Where does it come from? (Use ProfileMode)

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

Note

  • Only 32 bit floats are currently supported (development is in progress).
  • Shared variables with float32 dtype are by default moved to the GPU memory space.
  • There is a limit of one GPU per process.
  • Use the Theano flag device=gpu to require use of the GPU device.
  • Use device=gpu{0, 1, ...} to specify which GPU if you have more than one.
  • 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.

Solution


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 and Andreas Kloeckner’s website 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:

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

# (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).

Example: Theano + PyCUDA

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

class PyCUDADoubleOp(theano.Op):
    def __eq__(self, other):
        return type(self) == type(other)
    def __hash__(self):
        return hash(type(self))
    def __str__(self):
        return self.__class__.__name__
    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))
>>> xv=numpy.ones((4,5), dtype="float32")
>>> assert numpy.allclose(f(xv), xv*2)
>>> print numpy.asarray(f(xv))

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).