def backpropagate(self, ext, module, g_inp, g_out, backproped):
Curvature.check_loss_hessian(
self.derivatives.hessian_is_psd(), curv_type=ext.get_curv_type()
)
H_func = self.make_loss_hessian_func(ext)
H_loss = H_func(module, g_inp, g_out)
return H_loss
def backpropagate(self, ext, module, g_inp, g_out, backproped):
Curvature.check_loss_hessian(
self.derivatives.hessian_is_psd(),
curv_type=ext.get_curv_type()
)
hessian_strategy = ext.get_loss_hessian_strategy()
H_func = self.LOSS_HESSIAN_GETTERS[hessian_strategy]
H_loss = H_func(module, g_inp, g_out)
return H_loss
def backpropagate_batch_average(self, ext, module, g_inp, g_out, H):
ggn = self.derivatives.ea_jac_t_mat_jac_prod(module, g_inp, g_out, H)
residual = self.second_order_module_effects(module, g_inp, g_out)
residual_mod = Curvature.modify_residual(residual, ext.get_curv_type())
if residual_mod is not None:
ggn = self.add_diag_to_mat(residual_mod, ggn)
return ggn
def _second_order_module_effects(self, module, ext, g_inp, g_out):
if self.derivatives.hessian_is_zero():
return None
if not Curvature.require_residual(ext.get_curv_type()):
return None
if not self.derivatives.hessian_is_diagonal():
raise NotImplementedError(
"Residual terms are only supported for elementwise functions")
return self.derivatives.hessian_diagonal(module, g_inp, g_out)
def __make_nondiagonal_R_mat_prod(self, ext, module, g_inp, g_out, backproped):
curv_type = ext.get_curv_type()
if not Curvature.is_pch(curv_type):
R_mat_prod = self.derivatives.make_residual_mat_prod(module, g_inp, g_out)
else:
raise ValueError(
"{} not supported for {}. Residual cannot be cast PSD.".format(
curv_type, module
)
)
return R_mat_prod
def __make_diagonal_R_mat_prod(self, ext, module, g_inp, g_out, backproped):
# TODO Refactor core: hessian_diagonal -> residual_diagonal
R = self.derivatives.hessian_diagonal(module, g_inp, g_out)
R_mod = Curvature.modify_residual(R, ext.get_curv_type())
@R_mat_prod_accept_vectors
@R_mat_prod_check_shapes
def make_residual_mat_prod(self, module, g_inp, g_out):
def R_mat_prod(mat):
"""Multiply with the residual: mat Рєњ [РѕЉ_{k} Hz_k(x) ЮЏ┐z_k] mat.
Second term of the module input Hessian backpropagation equation.
"""
return einsum("n...,vn...->vn...", (R_mod, mat))
return R_mat_prod
return make_residual_mat_prod(self, module, g_inp, g_out)
def _require_residual(self, ext, module, g_inp, g_out, backproped):
"""Is the residual term required for multiply with the curvature?"""
vanishes = self.derivatives.hessian_is_zero()
neglected = not Curvature.require_residual(ext.get_curv_type())
return not (vanishes or neglected)
def backpropagate(self, ext, module, g_inp, g_out, backproped):
Curvature.check_loss_hessian(self.derivatives.hessian_is_psd(),
curv_type=ext.get_curv_type())
CMP = self.derivatives.hessian_matrix_product(module, g_inp, g_out)
return CMP
def _modify_residual(self, ext, residual):
return Curvature.modify_residual(residual, ext.get_curv_type())
