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Multiple additions to execute_with_gradients #6006

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Oct 22, 2022
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Multiple additions to execute_with_gradients #6006

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64 changes: 37 additions & 27 deletions ivy/functional/backends/jax/gradients.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,10 @@
# local
import ivy
from ivy.functional.ivy.gradients import (
_get_native_arrays_and_indices,
_zero_gradients_to_none_and_to_ivy,
_arrays_to_float_variables,
_get_required_native_variables,
_get_native_variables_and_indices,
_remove_zeros_and_nones,
_stop_grad_and_index,
)

Expand Down Expand Up @@ -49,47 +51,55 @@ def _set_duplicates(xs, duplicate_key_chains):
return xs


def _forward_fn(xs, func, duplicate_key_chains):
def _forward_fn(xs, x, xs_grad_idxs, func, duplicate_key_chains):
if xs_grad_idxs is not None:
ivy.set_nest_at_indices(xs, xs_grad_idxs, x)
else:
xs = x
if isinstance(xs, ivy.Container):
xs = _set_duplicates(xs, duplicate_key_chains)
ret = func(xs)
_, arr_values = _get_native_arrays_and_indices(ret)
if isinstance(arr_values, list) and len(arr_values) == 1:
arr_values = arr_values[0]
return arr_values
_, ret_values = _get_native_variables_and_indices(ret)
if isinstance(ret_values, list) and len(ret_values) == 1:
ret_values = ret_values[0]
return ret_values


def execute_with_gradients(func, xs, /, *, retain_grads=False, grad_idxs=None):
def execute_with_gradients(
func, xs, /, *, retain_grads=False, xs_grad_idxs=None, ret_grad_idxs=None
):
xs = _arrays_to_float_variables(xs)
func_ret = func(xs)
xs_required = _get_required_native_variables(ivy.copy_nest(xs), xs_grad_idxs)
xs = ivy.to_native(xs)
arr_idxs, arr_values = _get_native_arrays_and_indices(func_ret)

if arr_values is None or (isinstance(arr_values, list) and len(arr_values) == 0):
ret_idxs, ret_values = _get_native_variables_and_indices(func_ret)
if ret_values is None or (isinstance(ret_values, list) and len(ret_values) == 0):
return func_ret, {}
if isinstance(arr_values, list) and len(arr_values) == 1:
y = arr_values[0]
if isinstance(ret_values, list) and len(ret_values) == 1:
y = ret_values[0]
else:
y = arr_values

y = ret_values
duplicate_key_chains = ()
if isinstance(xs, ivy.Container):
duplicate_key_chains = xs.duplicate_array_keychains()

if isinstance(y, ivy.NativeArray):
grad_fn = jax.grad(lambda x: _forward_fn(x, func, duplicate_key_chains))
grads = grad_fn(xs)
grad_fn = jax.grad(
lambda x: _forward_fn(xs, x, xs_grad_idxs, func, duplicate_key_chains)
)
grads = grad_fn(xs_required)
else:
grad_fn = jax.jacrev(lambda x: _forward_fn(x, func, duplicate_key_chains))
grads_ = grad_fn(xs)
grad_fn = jax.jacrev(
lambda x: _forward_fn(xs, x, xs_grad_idxs, func, duplicate_key_chains)
)
grads_ = grad_fn(xs_required)
grads = grads_
if isinstance(arr_idxs, list) and len(arr_idxs):
grads = {arr_idxs[i]: grad for i, grad in enumerate(grads_)}

if isinstance(ret_idxs, list) and len(ret_idxs):
grads = {ret_idxs[i]: grad for i, grad in enumerate(grads_)}
if isinstance(xs, ivy.Container):
grads = _set_duplicates(grads, duplicate_key_chains)

grads = _zero_gradients_to_none_and_to_ivy(grads)
func_ret, grads = _stop_grad_and_index(func_ret, retain_grads, grads, grad_idxs)
grads = _remove_zeros_and_nones(grads, grads)
func_ret, grads = _stop_grad_and_index(func_ret, retain_grads, grads, ret_grad_idxs)
grads = ivy.to_ivy(grads)
return func_ret, grads


Expand All @@ -99,7 +109,7 @@ def value_and_grad(func):
def callback_fn(xs):
xs = ivy.nested_map(xs, lambda x: ivy.to_native(x), include_derived=True)
ret = jax.value_and_grad(grad_fn)(xs)
ret = _zero_gradients_to_none_and_to_ivy(ret)
ret = _remove_zeros_and_nones(ret, ret)
return ret

return callback_fn
Expand Down
4 changes: 3 additions & 1 deletion ivy/functional/backends/numpy/gradients.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,9 @@ def variable_data(x):
return x


def execute_with_gradients(func, xs, /, *, retain_grads=False, grad_idxs=None):
def execute_with_gradients(
func, xs, /, *, retain_grads=False, xs_grad_idxs=None, ret_grad_idxs=None
):
logging.warning(
"NumPy does not support autograd, "
"'execute_with_gradients' returns None in place of function gradients."
Expand Down
46 changes: 24 additions & 22 deletions ivy/functional/backends/tensorflow/gradients.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,8 +10,10 @@
# local
import ivy
from ivy.functional.ivy.gradients import (
_get_native_arrays_and_indices,
_zero_gradients_to_none_and_to_ivy,
_arrays_to_float_variables,
_get_required_native_variables,
_get_native_variables_and_indices,
_remove_zeros_and_nones,
_stop_grad_and_index,
)

Expand All @@ -29,23 +31,22 @@ def variable_data(x):
return x.value()


def execute_with_gradients(func, xs, /, *, retain_grads=False, grad_idxs=None):
def execute_with_gradients(
func, xs, /, *, retain_grads=False, xs_grad_idxs=None, ret_grad_idxs=None
):
xs = _arrays_to_float_variables(xs)
with tf.GradientTape(persistent=True, watch_accessed_variables=False) as tape:
tape.watch(ivy.to_native(xs))
func_ret = func(xs)
arr_idxs, arr_values = _get_native_arrays_and_indices(func_ret, reshape=False)

if arr_values is None or (isinstance(arr_values, list) and len(arr_values) == 0):
xs = _get_required_native_variables(xs, xs_grad_idxs)
ret_idxs, ret_values = _get_native_variables_and_indices(func_ret, reshape=False)
if ret_values is None or (isinstance(ret_values, list) and len(ret_values) == 0):
return func_ret, {}
if isinstance(arr_values, list) and len(arr_values) == 1:
y = arr_values[0]
if isinstance(ret_values, list) and len(ret_values) == 1:
y = ret_values[0]
else:
y = arr_values

def grad_func(y):
ret = tape.gradient(y, ivy.to_native(xs))
return ret

y = ret_values
grad_func = lambda y: tape.gradient(y, ivy.to_native(xs))
if isinstance(y, ivy.NativeArray):
grads = ivy.to_ivy(grad_func(y))
else:
Expand All @@ -57,16 +58,15 @@ def grad_func(y):
y = []
else:
y = ivy.multi_index_nest(y, array_idxs)

grads_ = ivy.nested_map(y, grad_func, include_derived=True)
grads = grads_
if isinstance(arr_idxs, list) and len(arr_idxs):
grads = {arr_idxs[i]: grad for i, grad in enumerate(grads_)}

grads = _zero_gradients_to_none_and_to_ivy(grads)
func_ret, grads = _stop_grad_and_index(func_ret, retain_grads, grads, grad_idxs)
if isinstance(ret_idxs, list) and len(ret_idxs):
grads = {ret_idxs[i]: grad for i, grad in enumerate(grads_)}
grads = _remove_zeros_and_nones(grads, grads)
func_ret, grads = _stop_grad_and_index(func_ret, retain_grads, grads, ret_grad_idxs)
if not retain_grads:
del tape
grads = ivy.to_ivy(grads)
return func_ret, grads


Expand All @@ -84,7 +84,8 @@ def grad_fn(xs):
lambda x: ivy.to_ivy(x),
include_derived=True,
)
grads = _zero_gradients_to_none_and_to_ivy(grads_)
grads_ = _remove_zeros_and_nones(grads_, grads_)
grads_ = ivy.to_ivy(grads_)
grad_idxs = ivy.nested_argwhere(grads_, lambda x: ivy.is_ivy_array(x))
grad_array_vals = list(ivy.multi_index_nest(grads_, grad_idxs))
xs = ivy.to_ivy(xs)
Expand Down Expand Up @@ -132,6 +133,7 @@ def callback_fn(x_in):
x_in = ivy.to_native(ivy.array(x_in))
tape.watch(x_in)
y = grad_fn(x_in)
return _zero_gradients_to_none_and_to_ivy(ivy.to_ivy(tape.gradient(y, x_in)))
grad_ = ivy.to_ivy(tape.gradient(y, x_in))
return _remove_zeros_and_nones(grad_, grad_)

return callback_fn
37 changes: 20 additions & 17 deletions ivy/functional/backends/torch/gradients.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -9,8 +9,10 @@
# local
import ivy
from ivy.functional.ivy.gradients import (
_get_native_arrays_and_indices,
_zero_gradients_to_none_and_to_ivy,
_arrays_to_float_variables,
_get_required_native_variables,
_get_native_variables_and_indices,
_remove_zeros_and_nones,
_stop_grad_and_index,
)

Expand All @@ -31,7 +33,6 @@ def variable_data(x):

def _forward_fn(xs, func):
xs = ivy.Container(xs)
print("xs", xs)
ret = func(xs)

if isinstance(ret, ivy.Array):
Expand All @@ -50,17 +51,19 @@ def _forward_fn(xs, func):


# noinspection PyShadowingNames
def execute_with_gradients(func, xs, /, *, retain_grads=False, grad_idxs=None):
def execute_with_gradients(
func, xs, /, *, retain_grads=False, xs_grad_idxs=None, ret_grad_idxs=None
):
xs = _arrays_to_float_variables(xs)
func_ret = func(xs)
xs = ivy.to_native(xs)
arr_idxs, arr_values = _get_native_arrays_and_indices(func_ret)

if arr_values is None or (isinstance(arr_values, list) and len(arr_values) == 0):
xs = _get_required_native_variables(xs, xs_grad_idxs)
ret_idxs, ret_values = _get_native_variables_and_indices(func_ret)
if ret_values is None or (isinstance(ret_values, list) and len(ret_values) == 0):
return func_ret, {}
if isinstance(arr_values, list) and len(arr_values) == 1:
y = arr_values[0]
if isinstance(ret_values, list) and len(ret_values) == 1:
y = ret_values[0]
else:
y = arr_values
y = ret_values

def grad_func(y):
if isinstance(xs, ivy.Container):
Expand Down Expand Up @@ -102,11 +105,11 @@ def grad_func(y):
grad_arr_values = ivy.multi_index_nest(y, grad_arr_idxs)
grads_ = [grad_func(torch.clone(arr_value)) for arr_value in grad_arr_values]
grads = grads_
if isinstance(arr_idxs, list) and len(arr_idxs):
grads = {arr_idxs[i]: grad for i, grad in enumerate(grads_)}

grads = _zero_gradients_to_none_and_to_ivy(grads)
func_ret, grads = _stop_grad_and_index(func_ret, retain_grads, grads, grad_idxs)
if isinstance(ret_idxs, list) and len(ret_idxs):
grads = {ret_idxs[i]: grad for i, grad in enumerate(grads_)}
grads = _remove_zeros_and_nones(grads, grads)
func_ret, grads = _stop_grad_and_index(func_ret, retain_grads, grads, ret_grad_idxs)
grads = ivy.to_ivy(grads)
return func_ret, grads


Expand All @@ -125,7 +128,7 @@ def autograd_fn(x):
else ivy.to_native(ivy.zeros_like(ivy.to_ivy(x)))
)
grad = ivy.to_ivy(grad)
grad = _zero_gradients_to_none_and_to_ivy(grad)
grad = _remove_zeros_and_nones(grads, grads)
return grad

grads = ivy.nested_map(
Expand Down
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