Add a script to help test GPU power consumption.

theano/misc/burn_gpu.py

+"""This script trigger convolution operation. We think it cause more
+GPU power consumption then gemm call.
+
+"""
+
+
+import numpy as np
+
+import theano
+import theano.tensor as T
+from theano.gpuarray import dnn
+from theano.tensor.nnet.abstract_conv import get_conv_output_shape
+
+
+def burn():
+    # This test that we have inplace work correctly even when
+    # GpuAllocEmpty get merged together.
+
+    sz = 128
+    img_shp = [sz, sz, sz, sz]
+    kern_shp = [sz//2, sz, 3, 3]
+    out_shp = get_conv_output_shape(img_shp, kern_shp, 'valid', (1, 1))
+    img = T.tensor4('img')
+    kern = T.tensor4('kern')
+    out = T.tensor4('out')
+    desc1 = dnn.GpuDnnConvDesc(border_mode='valid', conv_mode='conv')(
+        kern.shape)
+
+    def rand(shp):
+        return np.random.rand(*shp).astype(theano.config.floatX)
+
+    img = theano.shared(rand(img_shp))
+    kern = theano.shared(rand(kern_shp))
+    out = theano.shared(rand(out_shp))
+    # beta 1 is needed to force the reuse of out, otherwise, it is
+    # replaced by a GpuAllocEmpty
+    o1 = dnn._dnn_conv(img, kern, conv_mode='conv', out=out, beta=1.)
+    mode = theano.compile.get_default_mode().including(
+        "local_remove_all_assert")
+    f = theano.function([], [o1], mode=mode)
+    theano.printing.debugprint(f)
+    print("Start computation")
+    for i in range(10000):
+        f.fn()
+    print("Computation stopped")
+
+if __name__ == "__main__":
+    burn()