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Compile different functions for training and prediction modes. #25

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Compile different functions for training and prediction modes. #25

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30 changes: 18 additions & 12 deletions beacon8/layers/Module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,12 @@ class Module:
def __init__(self):
self.training_mode = True

self.fn_forward = None
self.fn_accum_grads = None
self.fn_accum_stats = None
# The functions are stored in a dictionary whose keys correspond to the
# values that `self.training_mode` can take. That way, it would be
# trivial to extend to further modes, and the code avoids many branches.
self.fn_forward = {}
self.fn_accum_grads = {}
self.fn_accum_stats = {}

#def __hash__(self):
# raise NotImplementedError("You *need* to reimplement hash, even if it's just python's default. See the documentation for more info.")
Expand Down Expand Up @@ -44,15 +47,18 @@ def symb_forward(self, symb_input):
raise NotImplementedError

def forward(self, data):
if self.fn_forward is None:
if self.training_mode not in self.fn_forward:
symb_in = _T.TensorType(_th.config.floatX, (False,) * data.ndim)('X')
symb_out = self.symb_forward(symb_in)
self.fn_forward = _th.function(inputs=[symb_in], outputs=symb_out)
self.fn_forward[self.training_mode] = _th.function(
inputs=[symb_in],
outputs=symb_out
)

return self.fn_forward(data)
return self.fn_forward[self.training_mode](data)

def accumulate_gradients(self, data_in, data_tgt, loss):
if self.fn_accum_grads is None:
if self.training_mode not in self.fn_accum_grads:
symb_in = _T.TensorType(_th.config.floatX, (False,) * data_in.ndim)('X')
symb_tgt = _T.TensorType(_th.config.floatX, (False,) * data_tgt.ndim)('T')
symb_out = self.symb_forward(symb_in)
Expand All @@ -62,18 +68,18 @@ def accumulate_gradients(self, data_in, data_tgt, loss):
symb_grads = _th.grad(cost=symb_err, wrt=params)

grads_updates = [(grad, grad + symb_grad) for grad, symb_grad in zip(grads, symb_grads)]
self.fn_accum_grads = _th.function(
self.fn_accum_grads[self.training_mode] = _th.function(
inputs=[symb_in, symb_tgt],
updates=grads_updates
)

self.fn_accum_grads(data_in, data_tgt)
self.fn_accum_grads[self.training_mode](data_in, data_tgt)

def get_stat_updates(self):
return []

def accumulate_statistics(self, data_in):
if self.fn_accum_stats is None:
if self.training_mode not in self.fn_accum_stats:
symb_in = _T.TensorType(_th.config.floatX, (False,) * data_in.ndim)('X')
self.symb_forward(symb_in)

Expand All @@ -83,9 +89,9 @@ def accumulate_statistics(self, data_in):
# compile and call a function. This prevents theano errors.
return

self.fn_accum_stats = _th.function(
self.fn_accum_stats[self.training_mode] = _th.function(
inputs=[symb_in],
updates=stat_updates
)

self.fn_accum_stats(data_in)
self.fn_accum_stats[self.training_mode](data_in)