def test_slicing(fargs_tests):
dims, dtype = fargs_tests
gpu = NervanaGPU(default_dtype=dtype)
cpu = NervanaCPU(default_dtype=dtype)
array_np = np.random.uniform(-1, 1, dims).astype(dtype)
array_ng = gpu.array(array_np, dtype=dtype)
array_nc = cpu.array(array_np, dtype=dtype)
assert_tensors_allclose(array_ng[0], array_nc[0], rtol=0, atol=1e-3)
assert_tensors_allclose(array_ng[-1], array_nc[-1], rtol=0, atol=1e-3)
assert_tensors_allclose(array_ng[0, :], array_nc[0, :], rtol=0, atol=1e-3)
assert_tensors_allclose(array_ng[0:], array_nc[0:], rtol=0, atol=1e-3)
assert_tensors_allclose(array_ng[:-1], array_nc[:-1], rtol=0, atol=1e-3)
assert_tensors_allclose(array_ng[:, 0], array_nc[:, 0], rtol=0, atol=1e-3)
assert_tensors_allclose(array_ng[:, 0:1], array_nc[:, 0:1], rtol=0, atol=1e-3)
assert_tensors_allclose(array_ng[-1, 0:], array_nc[-1:, 0:], rtol=0, atol=1e-3)
array_ng[0] = 0
array_nc[0] = 0
assert_tensors_allclose(array_ng, array_nc, rtol=0, atol=1e-3)
del(gpu)
if not compound:
opts = [ dict() ]
if ones:
vals = 1.0
else:
vals = (0.5 - ng.rand()) * 2
devI1 = ng.empty(dimI1, dtype=dtype)
devI2 = ng.empty(dimI2, dtype=dtype)
devO = ng.empty(dimO, dtype=dtype)
devI1[:] = vals
devI2[:] = vals
devO[:] = vals
cpuI1 = nc.array(devI1.get(), dtype=np.float64)
cpuI2 = nc.array(devI2.get(), dtype=np.float64)
cpuO = nc.array(devO.get(), dtype=np.float64)
if compound and opts is not update_opts:
devB = ng.empty((dimO[0], 1), dtype=np.float32)
devS = ng.empty((dimO[0], 1), dtype=np.float32)
devB[:] = vals
devS[:] = vals
cpuB = nc.array(devB.get(), dtype=np.float64)
cpuS = nc.array(devS.get(), dtype=np.float64)
if opts is bprop_opts:
devX = ng.empty(dimO, dtype=dtype)
devX[:] = vals
cpuX = nc.array(devX.get(), dtype=np.float64)
if not compound:
opts = [ dict() ]
if ones:
vals = 1.0
else:
vals = (0.5 - ng.rand()) * 2
devI1 = ng.empty(dimI1, dtype=dtype)
devI2 = ng.empty(dimI2, dtype=dtype)
devO = ng.empty(dimO, dtype=dtype)
devI1[:] = vals
devI2[:] = vals
devO[:] = vals
cpuI1 = nc.array(devI1.get(), dtype=np.float64)
cpuI2 = nc.array(devI2.get(), dtype=np.float64)
cpuO = nc.array(devO.get(), dtype=np.float64)
if compound and opts is not update_opts:
devB = ng.empty((dimO[0], 1), dtype=np.float32)
devS = ng.empty((dimO[0], 1), dtype=np.float32)
devB[:] = vals
devS[:] = vals
cpuB = nc.array(devB.get(), dtype=np.float64)
cpuS = nc.array(devS.get(), dtype=np.float64)
if opts is bprop_opts:
devX = ng.empty(dimO, dtype=dtype)
devX[:] = vals
cpuX = nc.array(devX.get(), dtype=np.float64)
