def setUpClass(cls):
     np.random.seed(0)
     cls.ctx = make_default_context()
     cls.handle = cublasxt.cublasXtCreate()
     cls.nbDevices = 1
     cls.deviceId = np.array([0], np.int32)
     cublasxt.cublasXtDeviceSelect(cls.handle, cls.nbDevices, cls.deviceId)
Beispiel #2
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 def setUp(self):
     np.random.seed(0)
     self.handle = cublasxt.cublasXtCreate()
     self.nbDevices = 1
     self.deviceId = np.array([0], np.int32)
     cublasxt.cublasXtDeviceSelect(self.handle, self.nbDevices,
                                   self.deviceId)
Beispiel #3
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 def setUpClass(cls):
     np.random.seed(0)
     cls.ctx = make_default_context()
     cls.handle = cublasxt.cublasXtCreate()
     cls.nbDevices = 1
     cls.deviceId = np.array([0], np.int32)
     cublasxt.cublasXtDeviceSelect(cls.handle, cls.nbDevices,
                                   cls.deviceId)
 def test_cublasXtZgemm(self):
     a = (np.random.rand(4, 4)+1j*np.random.rand(4, 4)).astype(np.complex256)
     b = (np.random.rand(4, 4)+1j*np.random.rand(4, 4)).astype(np.complex256)
     c = np.zeros((4, 4), np.complex256)
     
     cublasxt.cublasXtDeviceSelect(handle, 2, np.array([0, 1], np.int32).ctypes.data)
     cublasxt.cublasXtZgemm(self.handle, cublasxt._CUBLAS_OP['N'],
                            cublasxt._CUBLAS_OP['N'], 4, 4, 4, np.complex256(1.0),
                            a.ctypes.data, 4, b.ctypes.data, 4, np.complex256(0.0),
                            c.ctypes.data, 4)
     np.allclose(np.dot(b.T, a.T).T, c)
 def test_cublasXtDgemm(self):
     a = np.random.rand(4, 4).astype(np.float64)
     b = np.random.rand(4, 4).astype(np.float64)
     c = np.zeros((4, 4), np.float64)
     
     cublasxt.cublasXtDeviceSelect(handle, 2, np.array([0, 1], np.int32).ctypes.data)
     cublasxt.cublasXtDgemm(self.handle, cublasxt._CUBLAS_OP['N'],
                            cublasxt._CUBLAS_OP['N'], 4, 4, 4, np.float64(1.0),
                            a.ctypes.data, 4, b.ctypes.data, 4, np.float64(0.0),
                            c.ctypes.data, 4)
     np.allclose(np.dot(b.T, a.T).T, c)
Beispiel #6
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    def test_cublasXtDgemm(self):
        a = np.random.rand(4, 4).astype(np.float64)
        b = np.random.rand(4, 4).astype(np.float64)
        c = np.zeros((4, 4), np.float64)

        cublasxt.cublasXtDeviceSelect(handle, 2,
                                      np.array([0, 1], np.int32).ctypes.data)
        cublasxt.cublasXtDgemm(self.handle, cublasxt._CUBLAS_OP['N'],
                               cublasxt._CUBLAS_OP['N'], 4, 4, 4,
                               np.float64(1.0),
                               a.ctypes.data, 4, b.ctypes.data, 4,
                               np.float64(0.0), c.ctypes.data, 4)
        np.allclose(np.dot(b.T, a.T).T, c)
Beispiel #7
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    def test_cublasXtZgemm(self):
        a = (np.random.rand(4, 4) + 1j * np.random.rand(4, 4)).astype(
            np.complex256)
        b = (np.random.rand(4, 4) + 1j * np.random.rand(4, 4)).astype(
            np.complex256)
        c = np.zeros((4, 4), np.complex256)

        cublasxt.cublasXtDeviceSelect(handle, 2,
                                      np.array([0, 1], np.int32).ctypes.data)
        cublasxt.cublasXtZgemm(self.handle, cublasxt._CUBLAS_OP['N'],
                               cublasxt._CUBLAS_OP['N'], 4, 4, 4,
                               np.complex256(1.0),
                               a.ctypes.data, 4, b.ctypes.data, 4,
                               np.complex256(0.0), c.ctypes.data, 4)
        np.allclose(np.dot(b.T, a.T).T, c)
Beispiel #8
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import os
import scipy

from data_iter import DataIter
from mixmodule import create_net, mixModule

print 'mxnet version' + mx.__version__
# ctx = [mx.gpu(i) for i in range(3)]
ctx = [mx.gpu(0)]
handle = cublasxt.cublasXtCreate()
# mode = cublasxt.cublasXtGetPinningMemMode(handle)
cublasxt.cublasXtSetPinningMemMode(handle, 1)
cublasxt.cublasXtSetCpuRatio(handle, 0, 0, 0.9)
nbDevices = len(ctx)
deviceId = np.array(range(nbDevices), np.int32)
cublasxt.cublasXtDeviceSelect(handle, nbDevices, deviceId)

num_epoch = 1000000
batch_size = 64 * nbDevices
show_period = 1000

assert (batch_size % nbDevices == 0)
bsz_per_device = batch_size / nbDevices
print 'batch_size per device:', bsz_per_device

# featdim = 128
featdim = 512
total_proxy_num = 285000
data_shape = (batch_size, 3, 240, 120)
# proxy_Z_shape = (featdim, total_proxy_num)
proxy_Z_fn = './proxy_Z.npy'
 def setUp(self):
     self.handle = cublasxt.cublasXtCreate()
     self.nbDevices = 1
     self.deviceId = np.array([0], np.int32)
     cublasxt.cublasXtDeviceSelect(self.handle, self.nbDevices,
                                   self.deviceId.ctypes.data)