def test_to_pyro_module_():
pyro.set_rng_seed(123)
actual = nn.Sequential(
nn.Linear(28 * 28, 200),
nn.Sigmoid(),
nn.Linear(200, 200),
nn.Sigmoid(),
nn.Linear(200, 10),
)
to_pyro_module_(actual)
pyro.clear_param_store()
pyro.set_rng_seed(123)
expected = PyroModule[nn.Sequential](
PyroModule[nn.Linear](28 * 28, 200),
PyroModule[nn.Sigmoid](),
PyroModule[nn.Linear](200, 200),
PyroModule[nn.Sigmoid](),
PyroModule[nn.Linear](200, 10),
)
pyro.clear_param_store()
def assert_identical(a, e):
assert type(a) is type(e)
if isinstance(a, dict):
assert set(a) == set(e)
for key in a:
assert_identical(a[key], e[key])
elif isinstance(a, nn.Module):
assert_identical(a.__dict__, e.__dict__)
elif isinstance(a, (str, int, float, torch.Tensor)):
assert_equal(a, e)
assert_identical(actual, expected)
# check output
data = torch.randn(28 * 28)
actual_out = actual(data)
pyro.clear_param_store()
expected_out = expected(data)
assert_equal(actual_out, expected_out)
# check randomization
def randomize(model):
for m in model.modules():
for name, value in list(m.named_parameters(recurse=False)):
setattr(
m,
name,
PyroSample(
prior=dist.Normal(0, 1)
.expand(value.shape)
.to_event(value.dim())
),
)
randomize(actual)
randomize(expected)
assert_identical(actual, expected)
def as_pyro_module(module):
to_pyro_module_(module, recurse=True)
for m in module.modules():
for n, p in list(m.named_parameters(recurse=False)):
setattr(
m, n,
PyroSample(
dist.Normal(torch.zeros_like(p),
torch.ones_like(p)).to_event()))
return module
def test_name_preserved_by_to_pyro_module():
features = torch.randn(4)
data = torch.randn(3)
class Model(PyroModule):
def __init__(self):
super().__init__()
self.scale = PyroParam(torch.ones(3), constraints.positive)
self.loc = torch.nn.Linear(4, 3)
def forward(self, features, data):
loc = self.loc(features)
scale = self.scale
with pyro.plate("data", len(data)):
pyro.sample("obs", dist.Normal(loc, scale), obs=data)
model = Model()
params = list(model.parameters())
param_names = set()
def optim_config(param_name):
param_names.add(param_name)
return {"lr": 0.0}
# Record while model.loc is an nn.Module.
loss = poutine.trace(model).get_trace(features, data).log_prob_sum()
loss.backward()
adam = optim.Adam(optim_config)
adam(params)
assert param_names
expected_param_names = param_names.copy()
del adam, loss
param_names.clear()
pyro.clear_param_store()
# Record while model.loc is a PyroModule.
to_pyro_module_(model.loc)
loss = poutine.trace(model).get_trace(features, data).log_prob_sum()
loss.backward()
adam = optim.Adam(optim_config)
adam(params)
assert param_names
actual_param_names = param_names.copy()
del adam, loss
param_names.clear()
pyro.clear_param_store()
assert actual_param_names == {"scale", "loc.weight", "loc.bias"}
assert actual_param_names == expected_param_names
def test_bayesian_gru():
input_size = 2
hidden_size = 3
batch_size = 4
seq_len = 5
# Construct a simple GRU.
gru = nn.GRU(input_size, hidden_size)
input_ = torch.randn(seq_len, batch_size, input_size)
output, _ = gru(input_)
assert output.shape == (seq_len, batch_size, hidden_size)
output2, _ = gru(input_)
assert torch.allclose(output2, output)
# Make it Bayesian.
to_pyro_module_(gru)
for name, value in list(gru.named_parameters(recurse=False)):
prior = dist.Normal(0, 1).expand(value.shape).to_event(value.dim())
setattr(gru, name, PyroSample(prior=prior))
output, _ = gru(input_)
assert output.shape == (seq_len, batch_size, hidden_size)
output2, _ = gru(input_)
assert not torch.allclose(output2, output)
def encode(self, name: str, prior: Distribution):
"""
Apply encoder network to input data to obtain hidden layer encoding.
Parameters
----------
args
Pyro model args
kwargs
Pyro model kwargs
-------
"""
try:
args, kwargs = self.args_kwargs # stored as a tuple of (tuple, dict)
# get the data for NN from
in_names = self.amortised_plate_sites["input"]
x_in = [
kwargs[i] if i in kwargs.keys() else args[i] for i in in_names
]
# apply data transform before passing to NN
in_transforms = self.amortised_plate_sites["input_transform"]
x_in = [in_transforms[i](x) for i, x in enumerate(x_in)]
# apply learnable normalisation before passing to NN:
input_normalisation = self.amortised_plate_sites.get(
"input_normalisation", None)
if input_normalisation is not None:
for i in range(len(self.amortised_plate_sites["input"])):
if input_normalisation[i]:
x_in[i] = x_in[i] * deep_getattr(
self, f"input_normalisation_{i}")
if "single" in self.encoder_mode:
# encode with a single encoder
res = deep_getattr(self, "one_encoder")(*x_in)
if "multiple" in self.encoder_mode:
# when there is a second layer of multiple encoders fetch encoders and encode data
x_in[0] = res
res = deep_getattr(self.multiple_encoders, name)(*x_in)
else:
# when there are multiple encoders fetch encoders and encode data
res = deep_getattr(self.multiple_encoders, name)(*x_in)
return res
except AttributeError:
pass
# Initialize.
# create normalisation parameters if necessary:
input_normalisation = self.amortised_plate_sites.get(
"input_normalisation", None)
if input_normalisation is not None:
for i in range(len(self.amortised_plate_sites["input"])):
if input_normalisation[i]:
deep_setattr(
self,
f"input_normalisation_{i}",
PyroParam(
torch.ones((1, self.single_n_in)).to(
prior.mean.device).requires_grad_(True)),
)
# create encoder neural networks
if "single" in self.encoder_mode:
if self.encoder_instance is not None:
# copy provided encoder instance
one_encoder = deepcopy(self.encoder_instance).to(
prior.mean.device)
# convert to pyro module
to_pyro_module_(one_encoder)
deep_setattr(self, "one_encoder", one_encoder)
else:
# create encoder instance from encoder class
deep_setattr(
self,
"one_encoder",
self.encoder_class(n_in=self.single_n_in,
n_out=self.n_hidden["single"],
**self.encoder_kwargs).to(
prior.mean.device),
)
if "multiple" in self.encoder_mode:
# determine the number of hidden layers
if name in self.n_hidden.keys():
n_hidden = self.n_hidden[name]
else:
n_hidden = self.n_hidden["multiple"]
multi_encoder_kwargs = deepcopy(self.multi_encoder_kwargs)
multi_encoder_kwargs["n_hidden"] = n_hidden
# create multiple encoders
if self.encoder_instance is not None:
# copy instances
encoder_ = deepcopy(self.encoder_instance).to(
prior.mean.device)
# convert to pyro module
to_pyro_module_(encoder_)
deep_setattr(
self,
"multiple_encoders." + name,
encoder_,
)
else:
# create instances
deep_setattr(
self,
"multiple_encoders." + name,
self.encoder_class(n_in=self.multiple_n_in,
n_out=n_hidden,
**multi_encoder_kwargs).to(
prior.mean.device),
)
return self.encode(name, prior)
def __init__(
self,
model,
amortised_plate_sites: dict,
n_in: int,
n_hidden: dict = None,
init_param=0,
init_param_scale: float = 1 / 50,
scales_offset: float = -2,
encoder_class=FCLayersPyro,
encoder_kwargs=None,
multi_encoder_kwargs=None,
encoder_instance: torch.nn.Module = None,
create_plates=None,
encoder_mode: Literal["single", "multiple", "single-multiple"] = "single",
):
"""
Parameters
----------
model
Pyro model
amortised_plate_sites
Dictionary with amortised plate details:
the name of observation/minibatch plate,
indexes of model args to provide to encoder,
variable names that belong to the observation plate
and the number of dimensions in non-plate axis of each variable - such as:
{
"name": "obs_plate",
"input": [0], # expression data + (optional) batch index ([0, 2])
"input_transform": [torch.log1p], # how to transform input data before passing to NN
"sites": {
"n_s_cells_per_location": 1,
"y_s_groups_per_location": 1,
"z_sr_groups_factors": self.n_groups,
"w_sf": self.n_factors,
"l_s_add": 1,
}
}
n_in
Number of input dimensions (for encoder_class).
n_hidden
Number of hidden nodes in each layer, one of 3 options:
1. Integer denoting the number of hidden nodes
2. Dictionary with {"single": 200, "multiple": 200} denoting the number of hidden nodes for each `encoder_mode` (See below)
3. Allowing different number of hidden nodes for each model site. Dictionary with the number of hidden nodes for single encode mode and each model site:
{
"single": 200
"n_s_cells_per_location": 5,
"y_s_groups_per_location": 5,
"z_sr_groups_factors": 128,
"w_sf": 128,
"l_s_add": 5,
}
init_param
Not implemented yet - initial values for amortised variables.
init_param_scale
How to scale/normalise initial values for weights converting hidden layers to mean and sd.
encoder_class
Class for defining encoder network.
encoder_kwargs
Keyword arguments for encoder_class.
multi_encoder_kwargs
Optional separate keyword arguments for encoder_class, useful when encoder_mode == "single-multiple".
encoder_instance
Encoder network instance, overrides class input and the input instance is copied with deepcopy.
create_plates
Function for creating plates
encoder_mode
Use single encoder for all variables ("single"), one encoder per variable ("multiple")
or a single encoder in the first step and multiple encoders in the second step ("single-multiple").
"""
super().__init__(model, create_plates=create_plates)
self.amortised_plate_sites = amortised_plate_sites
self.encoder_mode = encoder_mode
self.scales_offset = scales_offset
self.softplus = SoftplusTransform()
if n_hidden is None:
n_hidden = {"single": 200, "multiple": 200}
else:
if isinstance(n_hidden, int):
n_hidden = {"single": n_hidden, "multiple": n_hidden}
elif not isinstance(n_hidden, dict):
raise ValueError("n_hidden must be either in or dict")
encoder_kwargs = encoder_kwargs if isinstance(encoder_kwargs, dict) else dict()
encoder_kwargs["n_hidden"] = n_hidden["single"]
self.encoder_kwargs = encoder_kwargs
if multi_encoder_kwargs is None:
multi_encoder_kwargs = deepcopy(encoder_kwargs)
self.multi_encoder_kwargs = multi_encoder_kwargs
if "multiple" in n_hidden.keys():
self.multi_encoder_kwargs["n_hidden"] = n_hidden["multiple"]
self.single_n_in = n_in
self.multiple_n_in = n_in
self.n_out = (
np.sum([np.sum(amortised_plate_sites["sites"][k]) for k in amortised_plate_sites["sites"].keys()]) * 2
)
self.n_hidden = n_hidden
self.encoder_class = encoder_class
self.encoder_instance = encoder_instance
if "single" in self.encoder_mode:
# create a single encoder NN
if encoder_instance is not None:
self.one_encoder = deepcopy(encoder_instance)
# convert to pyro module
to_pyro_module_(self.one_encoder)
else:
self.one_encoder = encoder_class(
n_in=self.single_n_in, n_out=self.n_hidden["single"], **self.encoder_kwargs
)
if "multiple" in self.encoder_mode:
self.multiple_n_in = self.n_hidden["single"]
self.init_param_scale = init_param_scale
def _setup_prototype(self, *args, **kwargs):
super()._setup_prototype(*args, **kwargs)
self._event_dims = {}
self._cond_indep_stacks = {}
self.hidden2locs = PyroModule()
self.hidden2scales = PyroModule()
if "multiple" in self.encoder_mode:
# create module for collecting multiple encoder NN
self.multiple_encoders = PyroModule()
# Initialize guide params
for name, site in self.prototype_trace.iter_stochastic_nodes():
# Collect unconstrained event_dims, which may differ from constrained event_dims.
with helpful_support_errors(site):
init_loc = biject_to(site["fn"].support).inv(site["value"].detach()).detach()
event_dim = site["fn"].event_dim + init_loc.dim() - site["value"].dim()
self._event_dims[name] = event_dim
# Collect independence contexts.
self._cond_indep_stacks[name] = site["cond_indep_stack"]
# determine the number of hidden layers
if "multiple" in self.encoder_mode:
if "multiple" in self.n_hidden.keys():
n_hidden = self.n_hidden["multiple"]
else:
n_hidden = self.n_hidden[name]
elif "single" in self.encoder_mode:
n_hidden = self.n_hidden["single"]
# add linear layer for locs and scales
param_dim = (n_hidden, self.amortised_plate_sites["sites"][name])
init_param = np.random.normal(
np.zeros(param_dim),
(np.ones(param_dim) * self.init_param_scale) / np.sqrt(n_hidden),
).astype("float32")
_deep_setattr(
self.hidden2locs,
name,
PyroParam(torch.tensor(init_param, device=site["value"].device, requires_grad=True)),
)
init_param = np.random.normal(
np.zeros(param_dim),
(np.ones(param_dim) * self.init_param_scale) / np.sqrt(n_hidden),
).astype("float32")
_deep_setattr(
self.hidden2scales,
name,
PyroParam(torch.tensor(init_param, device=site["value"].device, requires_grad=True)),
)
if "multiple" in self.encoder_mode:
# create multiple encoders
if self.encoder_instance is not None:
# copy instances
encoder_ = deepcopy(self.encoder_instance).to(site["value"].device)
# convert to pyro module
to_pyro_module_(encoder_)
_deep_setattr(
self.multiple_encoders,
name,
encoder_,
)
else:
# create instances
_deep_setattr(
self.multiple_encoders,
name,
self.encoder_class(n_in=self.multiple_n_in, n_out=n_hidden, **self.multi_encoder_kwargs).to(
site["value"].device
),
)
