def create_lstm_per_eg_grad(batch_size, state_size, steps):
inputs = [
random_ops.random_normal([batch_size, state_size]) for _ in range(steps)
]
cell = rnn_cell.BasicLSTMCell(state_size)
init_state = cell.zero_state(batch_size, dtypes.float32)
def model_fn(inps, init_state):
state = init_state
for inp in inps:
_, state = cell(inp, state)
output = nn.l2_loss(state.c)
return gradient_ops.gradients(output, variables.trainable_variables())
def loop_fn(i):
loop_inputs = [
array_ops.expand_dims(array_ops.gather(x, i), 0) for x in inputs
]
loop_init_state = rnn_cell.LSTMStateTuple(
*[array_ops.expand_dims(array_ops.gather(x, i), 0) for x in init_state])
return model_fn(loop_inputs, loop_init_state)
pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size)
loop_fn_dtypes = [x.dtype for x in variables.trainable_variables()]
while_outputs = control_flow_ops.for_loop(loop_fn, loop_fn_dtypes, batch_size)
return pfor_outputs, while_outputs
def batch_jacobian(output, inp, use_pfor=True):
"""Computes and stacks jacobians of `output[i,...]` w.r.t. `input[i,...]`.
e.g.
x = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
y = x * x
jacobian = batch_jacobian(y, x)
# => [[[2, 0], [0, 4]], [[6, 0], [0, 8]]]
Args:
output: A tensor with shape [b, y1, ..., y_n]. `output[i,...]` should
only depend on `inp[i,...]`.
inp: A tensor with shape [b, x1, ..., x_m]
use_pfor: If true, uses pfor for computing the Jacobian. Else uses a
tf.while_loop.
Returns:
A tensor `t` with shape [b, y_1, ..., y_n, x1, ..., x_m] where `t[i, ...]`
is the jacobian of `output[i, ...]` w.r.t. `inp[i, ...]`, i.e. stacked
per-example jacobians.
Raises:
ValueError: if first dimension of `output` and `inp` do not match.
"""
output_shape = output.shape
if not output_shape[0].is_compatible_with(inp.shape[0]):
raise ValueError("Need first dimension of output shape (%s) and inp shape "
"(%s) to match." % (output.shape, inp.shape))
if output_shape.is_fully_defined():
batch_size = int(output_shape[0])
output_row_size = output_shape.num_elements() // batch_size
else:
output_shape = array_ops.shape(output)
batch_size = output_shape[0]
output_row_size = array_ops.size(output) // batch_size
inp_shape = array_ops.shape(inp)
# Flatten output to 2-D.
with ops.control_dependencies(
[check_ops.assert_equal(batch_size, inp_shape[0])]):
output = array_ops.reshape(output, [batch_size, output_row_size])
def loop_fn(i):
y = array_ops.gather(output, i, axis=1)
return gradient_ops.gradients(y, inp)[0]
if use_pfor:
pfor_output = control_flow_ops.pfor(loop_fn, output_row_size)
else:
pfor_output = control_flow_ops.for_loop(loop_fn, output.dtype,
output_row_size)
if pfor_output is None:
return None
pfor_output = array_ops.reshape(pfor_output,
[output_row_size, batch_size, -1])
output = array_ops.transpose(pfor_output, [1, 0, 2])
new_shape = array_ops.concat([output_shape, inp_shape[1:]], axis=0)
return array_ops.reshape(output, new_shape)
def create_mnist_autobatch(batch_size, data_format, training):
images = random_ops.random_uniform([batch_size, 28, 28])
model = Mnist(data_format)
manual = model(images, training=training)
def loop_fn(i):
image = array_ops.gather(images, i)
return model(image, training=training)
pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size)
while_outputs = control_flow_ops.for_loop(
loop_fn, dtypes.float32, batch_size)
return pfor_outputs, while_outputs, manual
def benchmark_basic_while(self):
with ops.Graph().as_default():
def loop_fn(i):
_, s = control_flow_ops.while_loop(
lambda t, x: t < i,
lambda t, x: (t + 1, x + i),
[0, 0])
return s
iters = 50
pfor_output = pfor_control_flow_ops.pfor(loop_fn, iters)
for_loop_output = pfor_control_flow_ops.for_loop(loop_fn, dtypes.int32,
iters)
self._run(pfor_output, 100, name="pfor_basic")
self._run(for_loop_output, 100, name="for_loop_basic")
def create_mnist_per_eg_jacobian(batch_size, data_format, training):
images = random_ops.random_uniform([batch_size, 28, 28])
model = Mnist(data_format)
def loop_fn(i, use_pfor):
image = array_ops.gather(images, i)
logits = array_ops.reshape(model(image, training=training), [-1])
return gradients.jacobian(
logits, variables.trainable_variables(), use_pfor=use_pfor)
pfor_outputs = control_flow_ops.pfor(
functools.partial(loop_fn, use_pfor=True),
batch_size)
while_outputs = control_flow_ops.for_loop(
functools.partial(loop_fn, use_pfor=False),
[dtypes.float32] * len(variables.trainable_variables()), batch_size)
return pfor_outputs, while_outputs
def jacobian(output, inputs, use_pfor=True):
"""Computes jacobian of `output` w.r.t. `inputs`.
Args:
output: A tensor.
inputs: A tensor or a nested structure of tensor objects.
use_pfor: If true, uses pfor for computing the jacobian. Else uses
tf.while_loop.
Returns:
A tensor or a nested strucutre of tensors with the same structure as
`inputs`. Each entry is the jacobian of `output` w.rt. to the corresponding
value in `inputs`. If output has shape [y_1, ..., y_n] and inputs_i has
shape [x_1, ..., x_m], the corresponding jacobian has shape
[y_1, ..., y_n, x_1, ..., x_m].
"""
flat_inputs = nest.flatten(inputs)
output_tensor_shape = output.shape
output_shape = array_ops.shape(output)
output = array_ops.reshape(output, [-1])
def loop_fn(i):
y = array_ops.gather(output, i)
return gradient_ops.gradients(y, flat_inputs)
try:
output_size = int(output.shape[0])
except TypeError:
output_size = array_ops.shape(output)[0]
if use_pfor:
pfor_outputs = control_flow_ops.pfor(loop_fn, output_size)
else:
pfor_outputs = control_flow_ops.for_loop(
loop_fn, [output.dtype] * len(flat_inputs), output_size)
for i, out in enumerate(pfor_outputs):
if out is not None:
new_shape = array_ops.concat(
[output_shape, array_ops.shape(out)[1:]], axis=0)
out = array_ops.reshape(out, new_shape)
out.set_shape(output_tensor_shape.concatenate(flat_inputs[i].shape))
pfor_outputs[i] = out
return nest.pack_sequence_as(inputs, pfor_outputs)
def benchmark_matmul(self):
with ops.Graph().as_default():
n = 1024
params = 1000
x = random_ops.random_normal([n, params])
y = random_ops.random_normal([params, params])
def loop_fn(i):
x_i = array_ops.expand_dims(array_ops.gather(x, i), 0)
return math_ops.matmul(x_i, y)
pfor_outputs = pfor_control_flow_ops.pfor(loop_fn, n)
while_outputs = pfor_control_flow_ops.for_loop(loop_fn, dtypes.float32, n)
manual = math_ops.matmul(x, y)
self._run(manual, 1000, name="manual_matmul")
self._run(pfor_outputs, 1000, name="pfor_matmul")
self._run(while_outputs, 100, name="while_matmul")
def benchmark_add(self):
with ops.Graph().as_default():
n = 256
params = 1000
x = random_ops.random_normal([n, params])
y = random_ops.random_normal([n, params])
def loop_fn(i):
x_i = array_ops.gather(x, i)
y_i = array_ops.gather(y, i)
return x_i + y_i
pfor_outputs = pfor_control_flow_ops.pfor(loop_fn, n)
while_outputs = pfor_control_flow_ops.for_loop(loop_fn, dtypes.float32, n)
manual = x + y
self._run(manual, 1000, name="manual_add")
self._run(pfor_outputs, 1000, name="pfor_add")
self._run(while_outputs, 100, name="while_add")
def create_fc_per_eg_jacobians(batch_size, activation_size, num_layers):
model = FullyConnectedModel(activation_size=activation_size,
num_layers=num_layers)
inp = random_ops.random_normal([batch_size, activation_size])
output = model(inp)
jacobians = gradients.jacobian(output, variables.trainable_variables())
def loop_fn(i, use_pfor):
inp_i = array_ops.expand_dims(array_ops.gather(inp, i), 0)
output = array_ops.reshape(model(inp_i), [-1])
return gradients.jacobian(
output, variables.trainable_variables(), use_pfor=use_pfor)
per_eg_jacobians_pfor = control_flow_ops.pfor(
functools.partial(loop_fn, use_pfor=True),
batch_size)
per_eg_jacobians_while = control_flow_ops.for_loop(
functools.partial(loop_fn, use_pfor=False),
[dtypes.float32] * len(variables.trainable_variables()), batch_size)
return jacobians, per_eg_jacobians_pfor, per_eg_jacobians_while
def test_create_outside_and_write_and_scatter(self):
t = tensor_array_ops.TensorArray(dtypes.int32, 10, clear_after_read=False)
handle = t.handle
def loop_fn(i):
ta = t.write(i + 2, 2 * i).write(i, 5)
ta = ta.scatter([4 + i], [4]).scatter([6 + i, 8 + i], [6 + i, 8 + i])
return ta.flow
t1 = pfor_control_flow_ops.pfor(loop_fn, iters=2)
out1 = tensor_array_ops.TensorArray(
dtypes.int32, handle=handle, flow=t1[-1]).stack()
output1 = self._run_targets(out1)
t2 = pfor_control_flow_ops.for_loop(loop_fn, dtypes.float32, iters=2)
out2 = tensor_array_ops.TensorArray(
dtypes.int32, handle=handle, flow=t2[-1]).stack()
output2 = self._run_targets(out2)
self.assertAllClose(output2, output1)
def create_mnist_per_eg_grad(batch_size, data_format, training):
images = random_ops.random_uniform([batch_size, 28, 28])
sparse_labels = np.random.randint(
low=0, high=10, size=[batch_size]).astype(np.int32)
labels = np.zeros((batch_size, 10)).astype(np.float32)
labels[np.arange(batch_size), sparse_labels] = 1.
model = Mnist(data_format)
def loop_fn(i):
image = array_ops.gather(images, i)
label = array_ops.gather(labels, i)
logits = array_ops.reshape(model(image, training=training), [-1])
loss = losses.softmax_cross_entropy(
logits=logits, onehot_labels=label, reduction=losses.Reduction.NONE)
return gradient_ops.gradients(loss, variables.trainable_variables())
pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size)
while_outputs = control_flow_ops.for_loop(
loop_fn, [dtypes.float32] * len(variables.trainable_variables()),
batch_size)
return pfor_outputs, while_outputs
def create_fc_per_eg_grad(batch_size, activation_size, num_layers):
inp = random_ops.random_normal([batch_size, activation_size])
layers = [
tf_layers.Dense(activation_size, activation=nn.relu)
for _ in range(num_layers)
]
projection = tf_layers.Dense(1)
def model_fn(activation):
for layer in layers:
activation = layer(activation)
activation = projection(activation)
activation = nn.l2_loss(activation)
return gradient_ops.gradients(activation, variables.trainable_variables())
def loop_fn(i):
return model_fn(array_ops.expand_dims(array_ops.gather(inp, i), 0))
pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size)
loop_fn_dtypes = [x.dtype for x in variables.trainable_variables()]
while_outputs = control_flow_ops.for_loop(loop_fn, loop_fn_dtypes, batch_size)
return pfor_outputs, while_outputs
def create_mnist_per_eg_grad(batch_size, data_format, training):
images = random_ops.random_uniform([batch_size, 28, 28])
sparse_labels = np.random.randint(low=0, high=10,
size=[batch_size]).astype(np.int32)
labels = np.zeros((batch_size, 10)).astype(np.float32)
labels[np.arange(batch_size), sparse_labels] = 1.
model = Mnist(data_format)
def loop_fn(i):
image = array_ops.gather(images, i)
label = array_ops.gather(labels, i)
logits = array_ops.reshape(model(image, training=training), [-1])
loss = losses.softmax_cross_entropy(logits=logits,
onehot_labels=label,
reduction=losses.Reduction.NONE)
return gradient_ops.gradients(loss, variables.trainable_variables())
pfor_outputs = control_flow_ops.pfor(loop_fn, batch_size)
while_outputs = control_flow_ops.for_loop(
loop_fn, [dtypes.float32] * len(variables.trainable_variables()),
batch_size)
return pfor_outputs, while_outputs
def test_parallel_iterations_zero(self):
with self.assertRaisesRegexp(ValueError, "positive integer"):
pfor_control_flow_ops.pfor(lambda i: 1, 8, parallel_iterations=0)
with self.assertRaisesRegexp(TypeError, "positive integer"):
pfor_control_flow_ops.for_loop(lambda i: 1, dtypes.int32, 8,
parallel_iterations=0)
def test_parallel_iterations_zero(self):
with self.assertRaisesRegexp(ValueError, "positive integer"):
pfor_control_flow_ops.pfor(lambda i: 1, 8, parallel_iterations=0)
with self.assertRaisesRegexp(TypeError, "positive integer"):
pfor_control_flow_ops.for_loop(lambda i: 1, dtypes.int32, 8,
parallel_iterations=0)
def _test_loop_fn(self, loop_fn, iters, loop_fn_dtypes=dtypes.float32): t1 = pfor_control_flow_ops.pfor(loop_fn, iters=iters) t2 = pfor_control_flow_ops.for_loop(loop_fn, loop_fn_dtypes, iters=iters) self.run_and_assert_equal(t1, t2)
def batch_jacobian(output, inp, use_pfor=True, parallel_iterations=None):
"""Computes and stacks jacobians of `output[i,...]` w.r.t. `input[i,...]`.
e.g.
x = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
y = x * x
jacobian = batch_jacobian(y, x)
# => [[[2, 0], [0, 4]], [[6, 0], [0, 8]]]
Args:
output: A tensor with shape [b, y1, ..., y_n]. `output[i,...]` should
only depend on `inp[i,...]`.
inp: A tensor with shape [b, x1, ..., x_m]
use_pfor: If true, uses pfor for computing the Jacobian. Else uses a
tf.while_loop.
parallel_iterations: A knob to control how many iterations are vectorized
and dispatched in parallel. The default value of None, when use_pfor is
true, corresponds to vectorizing all the iterations. When use_pfor is
false, the default value of None corresponds to parallel_iterations=10.
This knob can be used to control the total memory usage.
Returns:
A tensor `t` with shape [b, y_1, ..., y_n, x1, ..., x_m] where `t[i, ...]`
is the jacobian of `output[i, ...]` w.r.t. `inp[i, ...]`, i.e. stacked
per-example jacobians.
Raises:
ValueError: if first dimension of `output` and `inp` do not match.
"""
output_shape = output.shape
if not output_shape[0].is_compatible_with(inp.shape[0]):
raise ValueError(
f"Need first dimension of `output` shape ({output.shape}) "
f"and `inp` shape ({inp.shape}) to match.")
if output_shape.is_fully_defined():
batch_size = int(output_shape[0])
output_row_size = output_shape.num_elements() // batch_size
else:
output_shape = array_ops.shape(output)
batch_size = output_shape[0]
output_row_size = array_ops.size(output) // batch_size
inp_shape = array_ops.shape(inp)
# Flatten output to 2-D.
with ops.control_dependencies(
[check_ops.assert_equal(batch_size, inp_shape[0])]):
output = array_ops.reshape(output, [batch_size, output_row_size])
def loop_fn(i):
y = array_ops.gather(output, i, axis=1)
return gradient_ops.gradients(y, inp)[0]
if use_pfor:
pfor_output = control_flow_ops.pfor(
loop_fn, output_row_size, parallel_iterations=parallel_iterations)
else:
pfor_output = control_flow_ops.for_loop(
loop_fn,
output.dtype,
output_row_size,
parallel_iterations=parallel_iterations)
if pfor_output is None:
return None
pfor_output = array_ops.reshape(pfor_output,
[output_row_size, batch_size, -1])
output = array_ops.transpose(pfor_output, [1, 0, 2])
new_shape = array_ops.concat([output_shape, inp_shape[1:]], axis=0)
return array_ops.reshape(output, new_shape)
def _test_loop_fn(self, loop_fn, iters, loop_fn_dtypes=dtypes.float32): t1 = pfor_control_flow_ops.pfor(loop_fn, iters=iters) t2 = pfor_control_flow_ops.for_loop(loop_fn, loop_fn_dtypes, iters=iters) self.run_and_assert_equal(t1, t2)
