Source code for mxnet.autograd

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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 == 1 or req == 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