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WIP: improvements to TorchForwardSimulator #479

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WIP: improvements to TorchForwardSimulator #479

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4682043
tweak stateless_data
rileyjmurray Jun 24, 2024
b0c630a
logging
rileyjmurray Jun 24, 2024
62f37ec
add trivial __getstate__ and __setstate__ needed for serialization
rileyjmurray Jul 16, 2024
f3bfb80
remove some logging and profiling code (well, just comment out)
rileyjmurray Jul 16, 2024
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tweak stateless_data
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@rileyjmurray
rileyjmurray committed Jun 24, 2024
commit 4682043f66f68b388f40aa3d9037df6719ebdaf0
16 changes: 9 additions & 7 deletions pygsti/modelmembers/operations/fulltpop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -164,16 +164,18 @@ def from_vector(self, v, close=False, dirty_value=True):
self._ptr_has_changed() # because _rep.base == _ptr (same memory)
self.dirty = dirty_value

def stateless_data(self) -> Tuple[int]:
return (self.dim,)

@staticmethod
def torch_base(sd: Tuple[int], t_param: _torch.Tensor) -> _torch.Tensor:
dim = sd[0]
def stateless_data(self) -> Tuple[int, _torch.Tensor]:
dim = self.dim
t_const = _torch.zeros(size=(1, dim), dtype=_torch.double)
t_const[0,0] = 1.0
t_param_mat = t_param.reshape((dim - 1, dim))
return (dim, t_const)

@staticmethod
def torch_base(sd: Tuple[int, _torch.Tensor], t_param: _torch.Tensor) -> _torch.Tensor:
dim, t_const = sd
t_param_mat = t_param.view(dim - 1, dim)
t = _torch.row_stack((t_const, t_param_mat))
# TODO: cache the row of all zeros?
return t


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23 changes: 11 additions & 12 deletions pygsti/modelmembers/povms/tppovm.py
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Expand Up @@ -102,29 +102,28 @@ def to_vector(self):
vec = _np.concatenate(effect_vecs)
return vec

def stateless_data(self) -> Tuple[int, _np.ndarray]:
def stateless_data(self) -> Tuple[int, _torch.Tensor, int]:
num_effects = len(self)
complement_effect = self[self.complement_label]
identity = complement_effect.identity.to_vector()
return (num_effects, identity)

@staticmethod
def torch_base(sd: Tuple[int, _np.ndarray], t_param: _torch.Tensor) -> _torch.Tensor:
num_effects, identity = sd
identity = identity.reshape((1, -1)) # make into a row vector
t_identity = _torch.from_numpy(identity)

dim = identity.size

first_basis_vec = _np.zeros(dim)
first_basis_vec[0] = dim ** 0.25
first_basis_vec = _np.zeros((1,dim))
first_basis_vec[0,0] = dim ** 0.25
TOL = 1e-15 * _np.sqrt(dim)
if _np.linalg.norm(first_basis_vec - identity) > TOL:
# Don't error out. The documentation for the class
# clearly indicates that the meaning of "identity"
# can be nonstandard.
warnings.warn('Unexpected normalization!')
return (num_effects, t_identity, dim)

identity = identity.reshape((1, -1)) # make into a row vector
t_identity = _torch.from_numpy(identity)
t_param_mat = t_param.reshape((num_effects - 1, dim))
@staticmethod
def torch_base(sd: Tuple[int, _torch.Tensor, int], t_param: _torch.Tensor) -> _torch.Tensor:
num_effects, t_identity, dim = sd
t_param_mat = t_param.view(num_effects - 1, dim)
t_func = t_identity - t_param_mat.sum(axis=0, keepdim=True)
t = _torch.row_stack((t_param_mat, t_func))
return t
11 changes: 6 additions & 5 deletions pygsti/modelmembers/states/tpstate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,13 +166,14 @@ def from_vector(self, v, close=False, dirty_value=True):
self._ptr_has_changed()
self.dirty = dirty_value

def stateless_data(self) -> Tuple[int]:
return (self.dim,)
def stateless_data(self) -> Tuple[_torch.Tensor]:
dim = self.dim
t_const = (dim ** -0.25) * _torch.ones(1, dtype=_torch.double)
return (t_const,)

@staticmethod
def torch_base(sd: Tuple[int], t_param: _torch.Tensor) -> _torch.Tensor:
dim = sd[0]
t_const = (dim ** -0.25) * _torch.ones(1, dtype=_torch.double)
def torch_base(sd: Tuple[_torch.Tensor], t_param: _torch.Tensor) -> _torch.Tensor:
t_const = sd[0]
t = _torch.concat((t_const, t_param))
return t

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