Python gpuarray.zeros函数代码示例

def test_elemwise_bool():
    a = gpuarray.empty((2,), context=context)
    exc = None
    try:
        bool(a)
    except ValueError as e:
        exc = e
    assert exc is not None
    a = gpuarray.zeros((1,), context=context)
    assert not bool(a)
    a = gpuarray.zeros((), context=context)
    assert not bool(a)

 def perform(self, node, inputs, outs):
     out, = outs
     v = inputs[0]
     sh = tuple(map(int, inputs[1:]))
     if out[0] is None or out[0].shape != sh:
         if self.memset_0:
             out[0] = gpuarray.zeros(sh, dtype=v.dtype)
         else:
             out[0] = gpuarray.empty(sh, dtype=v.dtype)
             out[0][...] = v
     else:
         out[0][...] = v
     if config.gpuarray.sync:
         out[0].sync()

 def perform(self, node, inputs, outs):
     out, = outs
     v = inputs[0]
     sh = tuple(map(int, inputs[1:]))
     if out[0] is None or out[0].shape != sh:
         if v.size == 1 and numpy.asarray(v)[0].item() == 0:
             out[0] = gpuarray.zeros(sh, dtype=v.dtype)
         else:
             out[0] = gpuarray.empty(sh, dtype=v.dtype)
             out[0][...] = v
     else:
         out[0][...] = v
     if config.gpuarray.sync:
         out[0].sync()

    def perform(self, node, inputs, outputs):
        (x,) = inputs
        (z,) = outputs

        dim = x.shape[0] + abs(self.offset)
        z[0] = gpuarray.zeros((dim, dim), dtype=x.dtype, context=x.context)

        if self.offset <= 0:  # diag in the lower triangle
            diag_z = z[0][-self.offset, :(dim + self.offset)]
        else:  # diag in the upper triangle
            diag_z = z[0][:(dim - self.offset), self.offset]
        diag_z.strides = (sum(z[0].strides),)

        diag_z[:] = x[:]

def test_shape():
    x = GpuArrayType(dtype='float32', broadcastable=[False, False, False])()
    v = gpuarray.zeros((3, 4, 5), dtype='float32', context=get_context(test_ctx_name))
    f = theano.function([x], x.shape)
    topo = f.maker.fgraph.toposort()
    assert np.all(f(v) == (3, 4, 5))
    if theano.config.mode != 'FAST_COMPILE':
        assert len(topo) == 4
        assert isinstance(topo[0].op, T.opt.Shape_i)
        assert isinstance(topo[1].op, T.opt.Shape_i)
        assert isinstance(topo[2].op, T.opt.Shape_i)
        assert isinstance(topo[3].op, T.opt.MakeVector)
    mode = mode_with_gpu.excluding("local_shape_to_shape_i")
    f = theano.function([x], x.shape, mode=mode)
    topo = f.maker.fgraph.toposort()
    assert np.all(f(v) == (3, 4, 5))
    assert len(topo) == 1
    assert isinstance(topo[0].op, T.Shape)

    assert out_c[1].shape == out_g[1].shape
    assert out_c[0].dtype == out_g[0].dtype
    assert out_c[1].dtype == out_g[1].dtype
    assert numpy.allclose(out_c[0], numpy.asarray(out_g[0]))
    assert numpy.allclose(out_c[1], numpy.asarray(out_g[1]))


def test_elemwise_bool():
    a = gpuarray.empty((2,), context=context)
    exc = None
    try:
        bool(a)
    except ValueError, e:
        exc = e
    assert e is not None
    a = gpuarray.zeros((1,), context=context)
    assert bool(a) == False
    a = gpuarray.zeros((), context=context)
    assert bool(a) == False


def test_broadcast():
    for shapea, shapeb in [((3, 5), (3, 5)),
                           ((1, 5), (3, 5)),
                           ((3, 5), (3, 1)),
                           ((1, 5), (3, 1)),
                           ((3, 1), (3, 5)),
                           ((3, 5), (3, 1)),
                           ((1, 1), (1, 1)),
                           ((3, 4, 5), (4, 5)),
                           ((4, 5), (3, 4, 5)),

def test_zero_noparam():
    try:
        gpu_ndarray.zeros()
        assert False
    except TypeError:
        pass

def test_zeros_no_dtype():
    # no dtype and order param
    x = gpu_ndarray.zeros((), context=ctx)
    y = numpy.zeros(())
    check_meta(x, y)

def zeros(shp, order, dtype):
    x = gpu_ndarray.zeros(shp, dtype, order, context=ctx)
    y = numpy.zeros(shp, dtype, order)
    check_all(x, y)

        def thunk():
            context = inputs[0][0].context

            # Size of the matrices to invert.
            z = outputs[0]

            # Matrix.
            A = inputs[0][0]

            # Solution vectors.
            b = inputs[1][0]

            assert(len(A.shape) == 2)
            assert(len(b.shape) == 2)

            if self.trans in ['T', 'C']:
                trans = 1
                l, n = A.shape
                k, m = b.shape
            elif self.trans == 'N':
                trans = 0
                n, l = A.shape
                k, m = b.shape
            else:
                raise ValueError('Invalid value for trans')
            if l != n:
                raise ValueError('A must be a square matrix')
            if n != k:
                raise ValueError('A and b must be aligned.')

            lda = max(1, n)
            ldb = max(1, k, m)

            # We copy A and b as cusolver operates inplace
            b = gpuarray.array(b, copy=True, order='F')
            if not self.inplace:
                A = gpuarray.array(A, copy=True)
            A_ptr = A.gpudata
            b_ptr = b.gpudata

            # cusolver expects a F ordered matrix, but A is not explicitly
            # converted between C and F order, instead we switch the
            # "transpose" flag.
            if A.flags['C_CONTIGUOUS']:
                trans = 1 - trans

            workspace_size = cusolver.cusolverDnSgetrf_bufferSize(
                cusolver_handle, n, n, A_ptr, lda)

            if (thunk.workspace is None or
                    thunk.workspace.size != workspace_size):
                thunk.workspace = gpuarray.zeros((workspace_size,),
                                                 dtype='float32',
                                                 context=context)

            if thunk.pivots is None or thunk.pivots.size != min(n, n):
                thunk.pivots = gpuarray.zeros((min(n, n),),
                                              dtype='float32',
                                              context=context)

            if thunk.dev_info is None:
                thunk.dev_info = gpuarray.zeros((1,),
                                                dtype='float32',
                                                context=context)

            workspace_ptr = thunk.workspace.gpudata
            pivots_ptr = thunk.pivots.gpudata
            dev_info_ptr = thunk.dev_info.gpudata

            cusolver.cusolverDnSgetrf(
                cusolver_handle, n, n, A_ptr, lda, workspace_ptr,
                pivots_ptr, dev_info_ptr)

            cusolver.cusolverDnSgetrs(
                cusolver_handle, trans, n, m, A_ptr, lda,
                pivots_ptr, b_ptr, ldb, dev_info_ptr)

            z[0] = b