def test_dense_var_to_tensor_read_dtype_same_as_var_dtype(self):
# read_dtype is same as dtype
v = resource_variable_ops.ResourceVariable(1.0, dtype=dtypes.float32)
v = resource_variable_ops._MixedPrecisionVariable(v, dtypes.float32)
if not context.executing_eagerly():
v.initializer.run()
# dtype is not read_dtype, return NotImplemented
self.assertEqual(
NotImplemented, v._dense_var_to_tensor(dtype=dtypes.float16))
self.assertEqual(NotImplemented,
v._dense_var_to_tensor(dtype=dtypes.float16, as_ref=True))
# as_ref is False
t = v._dense_var_to_tensor(as_ref=False)
self.assertTrue(isinstance(t, ops.Tensor))
self.assertEqual(t.dtype, dtypes.float32)
self.assertEqual(self.evaluate(t), 1.0)
t = v._dense_var_to_tensor(dtype=dtypes.float32, as_ref=False)
self.assertTrue(isinstance(t, ops.Tensor))
self.assertEqual(t.dtype, dtypes.float32)
self.assertEqual(self.evaluate(t), 1.0)
# as_ref is True
self.assertEqual(NotImplemented, v._dense_var_to_tensor(as_ref=True))
self.assertEqual(NotImplemented,
v._dense_var_to_tensor(dtype=dtypes.float32, as_ref=True))
def test_dense_var_to_tensor_read_dtype_same_as_var_dtype(self):
# read_dtype is same as dtype
v = resource_variable_ops.ResourceVariable(1.0, dtype=dtypes.float32)
v = resource_variable_ops._MixedPrecisionVariable(v, dtypes.float32)
if not context.executing_eagerly():
v.initializer.run()
# dtype is not read_dtype, return NotImplemented
self.assertEqual(
NotImplemented, v._dense_var_to_tensor(dtype=dtypes.float16))
self.assertEqual(NotImplemented,
v._dense_var_to_tensor(dtype=dtypes.float16, as_ref=True))
# as_ref is False
t = v._dense_var_to_tensor(as_ref=False)
self.assertTrue(isinstance(t, ops.Tensor))
self.assertEqual(t.dtype, dtypes.float32)
self.assertEqual(self.evaluate(t), 1.0)
t = v._dense_var_to_tensor(dtype=dtypes.float32, as_ref=False)
self.assertTrue(isinstance(t, ops.Tensor))
self.assertEqual(t.dtype, dtypes.float32)
self.assertEqual(self.evaluate(t), 1.0)
# as_ref is True
self.assertEqual(NotImplemented, v._dense_var_to_tensor(as_ref=True))
self.assertEqual(NotImplemented,
v._dense_var_to_tensor(dtype=dtypes.float32, as_ref=True))
def testFastpathExecute_MixedPrecisionVariableMatMulCorrectResponse(self):
ctx = context.context()
a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
m = resource_variable_ops.ResourceVariable(a_2_by_2)
m = resource_variable_ops._MixedPrecisionVariable(
m, read_dtype=dtypes.float16)
x = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a",
False, "transpose_b", False)
y = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16,
a_2_by_2_fp16, "transpose_a", False, "transpose_b", False)
self.assertEqual(x.dtype, dtypes.float16)
self.assertAllEqual(x, y)
def testFastpathExecute_MixedPrecisionVariableMatMulCorrectResponse(self):
ctx = context.context()
a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
m = resource_variable_ops.ResourceVariable(a_2_by_2)
m = resource_variable_ops._MixedPrecisionVariable(
m, read_dtype=dtypes.float16)
x = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a",
False, "transpose_b", False)
y = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16,
a_2_by_2_fp16, "transpose_a", False, "transpose_b", False)
self.assertEqual(x.dtype, dtypes.float16)
self.assertAllEqual(x, y)
def testFastpathExecute_MixedPrecisionVariableTapeWrite(self):
ctx = context.context()
with backprop.GradientTape(persistent=True) as tape:
a_2_by_2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]],
dtype=dtypes.float32)
a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
m1 = resource_variable_ops.ResourceVariable(a_2_by_2)
m2 = resource_variable_ops._MixedPrecisionVariable(
m1, read_dtype=dtypes.float16)
tape.watch(m2)
z = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16, m2,
"transpose_a", False, "transpose_b", False)
dz_dy = tape.gradient(z, [m2])[0]
self.assertEqual(dz_dy.dtype, dtypes.float16)
expected_grads = math_ops.matmul(
array_ops.transpose(a_2_by_2_fp16),
constant_op.constant(1., shape=[2, 2], dtype=dtypes.float16)).numpy()
self.assertAllEqual(dz_dy.numpy(), expected_grads)
def testFastpathExecute_MixedPrecisionVariableTapeWrite(self):
ctx = context.context()
with backprop.GradientTape(persistent=True) as tape:
a_2_by_2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]],
dtype=dtypes.float32)
a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
m1 = resource_variable_ops.ResourceVariable(a_2_by_2)
m2 = resource_variable_ops._MixedPrecisionVariable(
m1, read_dtype=dtypes.float16)
tape.watch(m2)
z = pywrap_tensorflow.TFE_Py_FastPathExecute(
ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16, m2,
"transpose_a", False, "transpose_b", False)
dz_dy = tape.gradient(z, [m2])[0]
self.assertEqual(dz_dy.dtype, dtypes.float16)
expected_grads = math_ops.matmul(
array_ops.transpose(a_2_by_2_fp16),
constant_op.constant(1., shape=[2, 2], dtype=dtypes.float16)).numpy()
self.assertAllEqual(dz_dy.numpy(), expected_grads)
