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# coding: utf-8
"""Autograd for NDArray."""
from __future__ import absolute_import
from __future__ import division
from array import array
from threading import Lock
import traceback
import ctypes
from ctypes import c_int, c_void_p, CFUNCTYPE, POINTER, cast
from .base import _LIB, check_call, string_types, mx_uint
from .base import NDArrayHandle, c_array, c_handle_array, c_array_buf, MXCallbackList, SymbolHandle
from .ndarray import NDArray, _ndarray_cls
from .ndarray import _GRAD_REQ_MAP
from .symbol import Symbol
[docs]def set_recording(is_recording): #pylint: disable=redefined-outer-name
"""Set status to recording/not recording. When recording, graph will be constructed
for gradient computation.
Parameters
----------
is_recording: bool
Returns
-------
previous state before this set.
"""
prev = ctypes.c_int()
check_call(_LIB.MXAutogradSetIsRecording(
ctypes.c_int(is_recording), ctypes.byref(prev)))
return bool(prev.value)
[docs]def set_training(train_mode): #pylint: disable=redefined-outer-name
"""Set status to training/predicting. This affects ctx.is_train in operator
running context. For example, Dropout will drop inputs randomly when
train_mode=True while simply passing through if train_mode=False.
Parameters
----------
train_mode: bool
Returns
-------
previous state before this set.
"""
prev = ctypes.c_int()
check_call(_LIB.MXAutogradSetIsTraining(
ctypes.c_int(train_mode), ctypes.byref(prev)))
return bool(prev.value)
[docs]def is_recording():
"""Get status on recording/not recording.
Returns
-------
Current state of recording.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsRecording(ctypes.byref(curr)))
return curr.value
[docs]def is_training():
"""Get status on training/predicting.
Returns
-------
Current state of training/predicting.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsTraining(ctypes.byref(curr)))
return curr.value
class _RecordingStateScope(object):
"""Scope for managing training state.
Example::
with _RecordingStateScope(True, True):
y = model(x)
backward([y])
"""
def __init__(self, is_record, train_mode): #pylint: disable=redefined-outer-name
self._enter_is_record = is_record
self._enter_train_mode = train_mode
self._prev_is_record = None
self._prev_train_mode = None
def __enter__(self):
if self._enter_is_record is not None:
self._prev_is_record = set_recording(self._enter_is_record)
if self._enter_train_mode is not None:
self._prev_train_mode = set_training(self._enter_train_mode)
def __exit__(self, ptype, value, trace):
if self._enter_is_record is not None and self._prev_is_record != self._enter_is_record:
set_recording(self._prev_is_record)
if self._enter_train_mode is not None and self._prev_train_mode != self._enter_train_mode:
set_training(self._prev_train_mode)
[docs]def record(train_mode=True): #pylint: disable=redefined-outer-name
"""Returns an autograd recording scope context to be used in 'with' statement
and captures code that needs gradients to be calculated.
.. note:: When forwarding with train_mode=False, the corresponding backward
should also use train_mode=False, otherwise gradient is undefined.
Example::
with autograd.record():
y = model(x)
backward([y])
metric.update(...)
optim.step(...)
Parameters
----------
train_mode: bool, default True
Whether the forward pass is in training or predicting mode. This controls the behavior
of some layers such as Dropout, BatchNorm.
"""
return _RecordingStateScope(True, train_mode)
[docs]def pause(train_mode=False): #pylint: disable=redefined-outer-name
"""Returns a scope context to be used in 'with' statement for codes that do not need
gradients to be calculated.
Example::
with autograd.record():
y = model(x)
backward([y])
with autograd.pause():
# testing, IO, gradient updates...
Parameters
----------
train_mode: bool, default False
Whether to do forward for training or predicting.
"""
return _RecordingStateScope(False, train_mode)
[docs]def train_mode():
"""Returns a scope context to be used in 'with' statement
in which forward pass behavior is set to training mode,
without changing the recording states.
Example::
y = model(x)
with autograd.train_mode():
y = dropout(y)
"""
return _RecordingStateScope(None, True)
[docs]def predict_mode():
"""Returns a scope context to be used in 'with' statement
in which forward pass behavior is set to inference mode,
without changing the recording states.
Example::
with autograd.record():
y = model(x)
with autograd.predict_mode():
y = sampling(y)
backward([y])
"""
return _RecordingStateScope(None, False)
[docs]def mark_variables(variables, gradients, grad_reqs='write'):
"""Mark NDArrays as variables to compute gradient for autograd.
Parameters
----------
variables: NDArray or list of NDArray
gradients: NDArray or list of NDArray
grad_reqs: str or list of str
"""
if isinstance(variables, NDArray):
assert isinstance(gradients, NDArray)
variables = [variables]
gradients = [gradients]
if isinstance(grad_reqs, string_types):
grad_reqs = [_GRAD_REQ_MAP[grad_reqs]]*len(variables)
else:
grad_reqs = [_GRAD_REQ_MAP[i] for i in grad_reqs]
check_call(_LIB.MXAutogradMarkVariables(
len(variables),
c_handle_array(variables),
c_array_buf(mx_uint, array('I', grad_reqs)),
c_handle_array(gradients)))
def _parse_head(heads, head_grads):
"""parse head gradient for backward and grad."""
if isinstance(heads, NDArray):
heads = [heads]
if isinstance(head_grads, NDArray):
head_grads = [head_grads]
head_handles = c_handle_array(heads)
if head_grads is None:
hgrad_handles = ctypes.c_void_p(0)
else:
assert len(heads) == len(head_grads), \
"heads and head_grads must be lists of the same length"
hgrad_handles = c_array(NDArrayHandle,
[i.handle if i is not None else NDArrayHandle(0)
for i in head_grads])
return head_handles, hgrad_handles
[docs]def backward(heads, head_grads=None, retain_graph=False, train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t previously marked variables.
Parameters
----------
heads: NDArray or list of NDArray
Output NDArray(s)
head_grads: NDArray or list of NDArray or None
Gradients with respect to heads.
train_mode: bool, optional
Whether to do backward for training or predicting.
"""
head_handles, hgrad_handles = _parse_head(heads, head_grads)
check_call(_LIB.MXAutogradBackwardEx(
len(head_handles),
head_handles,
hgrad_handles,
0,
ctypes.c_void_p(0),
ctypes.c_int(retain_graph),
ctypes.c_int(0),
ctypes.c_int(train_mode),
ctypes.c_void_p(0),
ctypes.c_void_p(0)))
[docs]def grad(heads, variables, head_grads=None, retain_graph=None, create_graph=False,
train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t variables. Gradients will be
returned as new NDArrays instead of stored into `variable.grad`.
Supports recording gradient graph for computing higher order gradients.
.. Note: Currently only a very limited set of operators support higher order
gradients.
Parameters
----------
heads: NDArray or list of NDArray
Output NDArray(s)
variables: NDArray or list of NDArray
Input variables to compute gradients for.
head_grads: NDArray or list of NDArray or None
Gradients with respect to heads.
retain_graph: bool
Whether to keep computation graph to differentiate again, instead
of clearing history and release memory. Defaults to the same value
as create_graph.
create_graph: bool
Whether to record gradient graph for computing higher order
train_mode: bool, optional
Whether to do backward for training or prediction.
Returns
-------
NDArray or list of NDArray:
Gradients with respect to variables.
Examples
--------
>>> x = mx.nd.ones((1,))
>>> x.attach_grad()
>>> with mx.autograd.record():
... z = mx.nd.elemwise_add(mx.nd.exp(x), x)
>>> dx = mx.autograd.grad(z, [x], create_graph=True)
>>> print(dx)
[
[ 3.71828175]
]
"""
head_handles, hgrad_handles = _parse_head(heads, head_grads)
if isinstance(variables, NDArray):
variables = [variables]
else:
assert len(variables), "variables cannot be an empty list."
var_handles = c_handle_array(variables)
retain_graph = retain_graph if retain_graph is not None else create_graph
grad_vars = ctypes.POINTER(NDArrayHandle)()
grad_stypes = ctypes.POINTER(ctypes.c_int)()
check_call(_LIB.MXAutogradBackwardEx(
len(head_handles),
head_handles,
hgrad_handles,
len(var_handles),
var_handles,
ctypes.c_int(retain_graph),
ctypes.c_int(create_graph),
ctypes.c_int(train_mode),
ctypes.byref(grad_vars),
ctypes.byref(grad_stypes)))
ret = [_ndarray_cls(ctypes.cast(grad_vars[i], NDArrayHandle),
stype=grad_stypes[i])
for i in range(len(var_handles))]
if isinstance(variables, NDArray):
return ret[0]
return ret
[docs]def get_symbol(x):
"""Retrieve recorded computation history as `Symbol`.
Parameters
----------
x : NDArray
Array representing the head of computation graph.
Returns
-------
Symbol
The retrieved Symbol.
"""
hdl = SymbolHandle()
check_call(_LIB.MXAutogradGetSymbol(x.handle, ctypes.byref(hdl)))
return Symbol(hdl)
[docs]class Function(object):
"""Customize differentiation in autograd.
If you don't want to use the gradients computed by the default
chain-rule, you can use Function to customize differentiation for
computation. You define your computation in
the forward method and provide the customized differentiation
in the backward method. During gradient computation, autograd will
use the user-defined backward function instead of the default chain-rule.
You can also cast to numpy array and back for some operations in
forward and backward.
For example, a stable sigmoid function can be defined as::
class sigmoid(mx.autograd.Function):
def forward(self, x):
y = 1 / (1 + mx.nd.exp(-x))
self.save_for_backward(y)
return y
def backward(self, dy):
# backward takes as many inputs as forward's return value,
# and returns as many NDArrays as forward's arguments.
y, = self.saved_tensors
return dy * y * (1-y)
Then, the function can be used in the following way::
func = sigmoid()
x = mx.nd.random.uniform(shape=(10,))
x.attach_grad()
with mx.autograd.record():
m = func(x)
m.backward()
dx = x.grad.asnumpy()
"""
_bwd_functype = CFUNCTYPE(c_int, c_int, c_int, POINTER(c_void_p),
POINTER(c_int), c_int, c_void_p)
_del_functype = CFUNCTYPE(c_int, c_void_p)
class _Registry(object):
"""CustomOp registry."""
def __init__(self):
self.ref_holder = {}
self.counter = 0
self.lock = Lock()
def inc(self):
"""Get index for new entry."""
self.lock.acquire()
cur = self.counter
self.counter += 1
self.lock.release()
return cur
_registry = _Registry()
def __init__(self):
self._used = False
self.saved_tensors = ()
def save_for_backward(self, *args):
self.saved_tensors = args
def __call__(self, *inputs):
assert not self._used, \
"Each Function instance can only be called once. "\
"Please create another instance."
self._used = True
prev_recording = set_recording(False)
outputs = self.forward(*inputs)
set_recording(prev_recording)
if not prev_recording:
return outputs
ret_outputs = outputs
if isinstance(outputs, NDArray):
outputs = (outputs,)
key = Function._registry.inc()
def backward_entry(num_ograds, num_igrads, ptrs, reqs, is_train, _):
"""entry point for backward."""
# pylint: disable=W0613
try:
output_grads = [NDArray(ctypes.cast(i, NDArrayHandle), writable=False) \
for i in ptrs[:num_ograds]]
input_grads = [NDArray(ctypes.cast(i, NDArrayHandle), writable=True) \
for i in ptrs[num_ograds:num_ograds+num_igrads]]
reqs = [reqs[i] for i in range(num_igrads)]
rets = self.backward(*output_grads)
if isinstance(rets, NDArray):
rets = (rets,)
assert len(rets) == len(input_grads), \
"%s.backward must return exactly the same number " \
"of NDArrays as the number of NDArrays arguments to forward." \
"Expecting %d got %d"%(self.__class__.name, len(input_grads), len(rets))
for igrad, ret, req in zip(input_grads, rets, reqs):
assert isinstance(ret, NDArray), \
"autograd.Function.backward must return NDArrays, not %s"%type(ret)
if req == 0: # null
return True
elif req in (1, 2): # write or inplace
igrad[:] = ret
elif req == 'add':
igrad[:] += ret
except Exception: # pylint: disable=broad-except
print('Error in Function.backward: %s' % traceback.format_exc())
return False
return True
def delete_entry(_):
"""C Callback for CustomFunction::delete"""
try:
del Function._registry.ref_holder[key]
except Exception: # pylint: disable=broad-except
print('Error in autograd.Function.delete: %s' % traceback.format_exc())
return False
return True
callbacks = [Function._bwd_functype(backward_entry),
Function._del_functype(delete_entry)]
callbacks = [cast(i, CFUNCTYPE(c_int)) for i in callbacks]
context = MXCallbackList(c_int(len(callbacks)),
cast(c_array(CFUNCTYPE(c_int), callbacks),
POINTER(CFUNCTYPE(c_int))),
cast(c_array(c_void_p, [None]*len(callbacks)),
POINTER(c_void_p)))
check_call(_LIB.MXCustomFunctionRecord(
c_int(len(inputs)),
c_handle_array(inputs),
c_int(len(outputs)),
c_handle_array(outputs),
ctypes.byref(context)))
Function._registry.ref_holder[key] = context
return ret_outputs
[docs] def forward(self, *inputs):
"""Forward computation."""
raise NotImplementedError
[docs] def backward(self, *output_grads):
"""Backward computation.
Takes as many inputs as forward's outputs,
and returns as many NDArrays as forward's inputs.
"""
raise NotImplementedError
