def setUp(self):
ipt = np.array(np.random.randint(10, size=[10, 10])).astype('uint16')
self.inputs = {'X': ipt}
self.outputs = {'Out': convert_uint16_to_float(ipt)}
self.attrs = {
'out_dtype': int(core.VarDesc.VarType.FP32),
'in_dtype': int(core.VarDesc.VarType.BF16)
}
self.op_type = 'transfer_dtype'
def verify_output(self, outs):
if np.array(outs[0]).dtype == np.uint16:
result = convert_uint16_to_float(np.array(outs[0]))
else:
result = np.array(outs[0])
hist, prob = self.output_hist(result)
self.assertTrue(np.allclose(hist, prob, rtol=0, atol=0.01),
"hist: " + str(hist))
def setUp(self):
ipt = np.array(np.random.randint(10, size=[10, 10])).astype('uint16')
self.inputs = {'X': ipt}
self.outputs = {'Out': convert_uint16_to_float(ipt)}
self.attrs = {
'in_dtype': int(core.VarDesc.VarType.BF16),
'out_dtype': int(core.VarDesc.VarType.FP32)
}
self.op_type = 'cast'
self.__class__.no_need_check_grad = True
def check_with_place(self, place):
scope = core.Scope()
out = scope.var("X").get_selected_rows()
paddle.seed(10)
op = Operator("uniform_random",
Out="X",
shape=[1000, 784],
min=-5.0,
max=10.0,
seed=10,
dtype=int(core.VarDesc.VarType.BF16))
op.run(scope, place)
self.assertEqual(out.get_tensor().shape(), [1000, 784])
result = convert_uint16_to_float(np.array(out.get_tensor()))
hist, prob = output_hist(result)
self.assertTrue(np.allclose(hist, prob, rtol=0, atol=0.01),
"hist: " + str(hist))
def test_check_output(self):
place = core.CPUPlace()
scope = core.Scope()
out = scope.var("X").get_selected_rows()
shape_tensor = scope.var("Shape").get_tensor()
shape_tensor.set(np.array([1000, 784]).astype("int64"), place)
paddle.seed(10)
op = Operator("uniform_random",
ShapeTensor="Shape",
Out="X",
min=-5.0,
max=10.0,
seed=10,
dtype=int(core.VarDesc.VarType.BF16))
op.run(scope, place)
self.assertEqual(out.get_tensor().shape(), [1000, 784])
result = convert_uint16_to_float(np.array(out.get_tensor()))
hist, prob = output_hist(result)
self.assertTrue(np.allclose(hist, prob, rtol=0, atol=0.01),
"hist: " + str(hist))
