def fn(global_step):
"""Returns a learning rate given the current training iteration."""
float_training_steps = ff(training_steps)
global_step = ff(global_step)
# ensure we don't train longer than training steps
global_step = jnp.minimum(global_step, float_training_steps)
constant_steps = float_training_steps * constant_fraction
x = jnp.maximum(ff(global_step), ff(constant_steps))
min_learning_rate = min_learning_rate_mult * learning_rate
if warmup_fraction:
min_warmup_fraction = jnp.maximum(warmup_fraction,
constant_fraction)
warmup_steps = float_training_steps * min_warmup_fraction
is_warmup = ff(jnp.greater(ff(warmup_steps), ff(global_step)))
warmup_lr = (global_step / warmup_steps) * learning_rate
else:
warmup_lr = learning_rate
is_warmup = 0.0
step = x - constant_steps
constant_and_decay = (learning_rate - min_learning_rate) * (
jnp.cos(step * onp.pi /
(float_training_steps - constant_steps)) / 2.0 +
0.5) + min_learning_rate
new_learning_rate = constant_and_decay * (
1.0 - is_warmup) + is_warmup * (warmup_lr)
return new_learning_rate
def _localization_loss(pred_locs, gt_locs, gt_labels, num_matched_boxes):
"""Computes the localization loss.
Computes the localization loss using smooth l1 loss.
Args:
pred_locs: a dict from index to tensor of predicted locations. The shape
of each tensor is [batch_size, num_anchors, 4].
gt_locs: a list of tensors representing box regression targets in
[batch_size, num_anchors, 4].
gt_labels: a list of tensors that represents the classification groundtruth
targets. The shape is [batch_size, num_anchors, 1].
num_matched_boxes: the number of anchors that are matched to a groundtruth
targets, used as the loss normalizater. The shape is [batch_size].
Returns:
box_loss: a float32 representing total box regression loss.
"""
keys = sorted(pred_locs.keys())
box_loss = 0
for i, k in enumerate(keys):
gt_label = gt_labels[i]
gt_loc = gt_locs[i]
pred_loc = jnp.reshape(pred_locs[k], gt_loc.shape)
mask = jnp.greater(gt_label, 0)
float_mask = mask.astype(jnp.float32)
smooth_l1 = jnp.sum(huber_loss(gt_loc, pred_loc), axis=-1)
smooth_l1 = jnp.multiply(smooth_l1, float_mask)
box_loss = box_loss + jnp.sum(
smooth_l1, axis=list(range(1, len(smooth_l1.shape))))
return jnp.mean(box_loss / num_matched_boxes)
def batch_codebook_coverage(codes: JTensor,
num_classes: int,
*,
paddings: JTensor,
data_parallel_axis: Optional[str] = None):
"""Computes codebook coverage within a batch.
Args:
codes: [..., num_groups], values are in [0, num_classes).
num_classes: A Python int.
paddings: [...], 0/1 value tensor.
data_parallel_axis: If set will psum() over the axis
Returns:
A scalar tf.Tensor, avg coverage across groups.
"""
# [num_groups, num_classes]
_, _, histogram = batch_pplx_entropy_from_codes(
codes,
num_classes,
paddings=paddings,
data_parallel_axis=data_parallel_axis)
onehot = jnp.greater(histogram, 0).astype(jnp.float32)
avg_num_covered_words = jnp.mean(jnp.sum(onehot, -1))
return avg_num_covered_words / num_classes
def forward_fn(data: Mapping[str, jnp.ndarray],
is_training: bool = True) -> jnp.ndarray:
"""Forward pass."""
tokens = data['obs']
input_mask = jnp.greater(tokens, 0)
seq_length = tokens.shape[1]
# Embed the input tokens and positions.
embed_init = hk.initializers.TruncatedNormal(stddev=0.02)
token_embedding_map = hk.Embed(vocab_size, d_model, w_init=embed_init)
token_embs = token_embedding_map(tokens)
positional_embeddings = hk.get_parameter('pos_embs',
[seq_length, d_model],
init=embed_init)
input_embeddings = token_embs + positional_embeddings
# Run the transformer over the inputs.
transformer = model.Transformer(num_heads=num_heads,
num_layers=num_layers,
dropout_rate=dropout_rate)
output_embeddings = transformer(input_embeddings, input_mask,
is_training)
# Reverse the embeddings (untied).
return hk.Linear(vocab_size)(output_embeddings)
def jax_mv_normal_entropy(cov): k = cov.shape[0] eigvals = np.linalg.eigvalsh(cov) eps = 1e-5*np.max(abs(eigvals)) mask = np.greater(abs(eigvals)-eps, 0.) log_nonzero_eigvals = np.where(mask, np.log(eigvals), np.zeros_like(eigvals)) log_det = np.sum(log_nonzero_eigvals) return k/2. + (k/2.)*np.log(2*np.pi) + .5*log_det
def insert(m, r, i): n = m.shape[0] a = np.concatenate([m, r[np.newaxis,:]], axis=0) before_inds = np.arange(n+1)*np.less(np.arange(n+1),i) after_inds = (np.arange(n+1)-1)*np.greater(np.arange(n+1),i) new_ind = np.ones(shape=[n+1], dtype=np.int32)*np.equal(np.arange(n+1),i)*n inds = before_inds + after_inds + new_ind return a[inds]
def _iter_body(state):
"""One step of power iteration."""
i, new_v, s, s_v, unused_run_step = state
new_v = new_v / jnp.linalg.norm(new_v)
s_v = jnp.einsum('ij,j->i', matrix, new_v, precision=precision)
s_new = jnp.einsum('i,i->', new_v, s_v, precision=precision)
return (i + 1, s_v, s_new, s_v,
jnp.greater(jnp.abs(s_new - s), error_tolerance))
def i_stimulus(t, params_and_data):
return lax.cond(
np.logical_or(
np.less(t, params_and_data["stimulus_start_time"]),
np.greater(t, params_and_data["stimulus_end_time"]),
),
lambda _: 0.0,
lambda _: params_and_data["i_stimulus"],
None,
)
def lm_loss_fn(forward_fn, vocab_size, params, rng, data, is_training=True):
"""Compute the loss on data wrt params."""
logits = forward_fn(params, rng, data, is_training)
targets = hk.one_hot(data['target'], vocab_size)
assert logits.shape == targets.shape
mask = jnp.greater(data['obs'], 0)
loss = -jnp.sum(targets * jax.nn.log_softmax(logits), axis=-1)
loss = jnp.sum(loss * mask) / jnp.sum(mask)
return loss
def embeddings(data: Mapping[str, jnp.ndarray], vocab_size: int):
tokens = data['obs']
input_mask = jnp.greater(tokens, 0)
seq_length = tokens.shape[1]
# Embed the input tokens and positions.
embed_init = hk.initializers.TruncatedNormal(stddev=0.02)
token_embedding_map = hk.Embed(vocab_size, d_model, w_init=embed_init)
token_embs = token_embedding_map(tokens)
positional_embeddings = hk.get_parameter('pos_embs', [seq_length, d_model],
init=embed_init)
input_embeddings = token_embs + positional_embeddings
return input_embeddings, input_mask
def single_iteration_condition(args):
"""Checks if the acceptance ratio or maximum iterations is reached
Parameters
----------
args : tuple
loop variables (described in `single_iteration`)
Returns
-------
bool:
True if acceptance_ratio is reached or the maximum number of
iterations is reached
"""
return np.logical_and(np.greater(args[-3], acceptance_ratio),
np.less(args[-2], max_iteration))
def _update_controller(args):
counter = args[0]
jax.lax.cond(
np.logical_and(
np.logical_and(
np.greater(counter, 0),
np.equal(counter % print_rate, 0)),
np.not_equal(counter, max_iterations - remainder)),
lambda _: id_tap(
_update_pbar, (print_rate,) + args),
lambda _: (print_rate,) + args,
operand=None)
jax.lax.cond(
np.equal(counter, max_iterations - remainder),
lambda _: id_tap(
_update_pbar, (remainder,) + args),
lambda _: (remainder,) + args,
operand=None)
def forward_fn(data: Mapping[str, jnp.ndarray],
is_training: bool = True) -> jnp.ndarray:
"""Forward pass."""
tokens = data['obs']
input_mask = jnp.greater(tokens, 0)
batch_size, seq_length = tokens.shape
# Embed the input tokens and positions.
embed_init = hk.initializers.TruncatedNormal(stddev=0.02)
token_embedding_map = hk.Embed(vocab_size, d_model, w_init=embed_init)
input_embeddings = token_embedding_map(tokens)
positional_embeddings = hk.get_parameter('pos_embs',
[seq_length, d_model],
init=embed_init)
x = input_embeddings + positional_embeddings
h = jnp.zeros_like(x)
# Create transformer block
transformer_block = model.UTBlock(num_heads=num_heads,
num_layers=num_layers,
dropout_rate=dropout_rate)
transformed_net = hk.transform(transformer_block)
# lift params
inner_params = hk.experimental.lift(transformed_net.init)(
hk.next_rng_key(), h, x, input_mask, is_training)
def f(_params, _rng, _z, *args):
return transformed_net.apply(_params,
_rng,
_z,
*args,
is_training=is_training)
z_star = deq(inner_params, hk.next_rng_key(), h, f, max_iter, x,
input_mask)
# Reverse the embeddings (untied).
return hk.Linear(vocab_size)(z_star)
def _greater(a, b):
return jnp.greater(a, b)
def pi_adjusted_inverse(
factor_0: jnp.ndarray,
factor_1: jnp.ndarray,
damping: jnp.ndarray,
pmap_axis_name: str,
) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""Performs inversion with pi-adjusted damping."""
# Compute the norms of each factor
norm_0 = jnp.trace(factor_0)
norm_1 = jnp.trace(factor_1)
# We need to sync the norms here, because reduction can be non-deterministic.
# They specifically are on GPUs by default for better performance.
# Hence although factor_0 and factor_1 are synced, the trace operation above
# can still produce different answers on different devices.
norm_0, norm_1 = pmean_if_pmap((norm_0, norm_1), axis_name=pmap_axis_name)
# Compute the overall scale
scale = norm_0 * norm_1
def regular_inverse(
operand: Sequence[jnp.ndarray]) -> Tuple[jnp.ndarray, jnp.ndarray]:
factor0, factor1, norm0, norm1, s, d = operand
# Special cases with one or two scalar factors
if factor0.size == 1 and factor1.size == 1:
value = jnp.ones_like(factor0) / jnp.sqrt(s)
return value, value
if factor0.size == 1:
factor1_normed = factor1 / norm1
damping1 = d / norm1
factor1_inv = psd_inv_cholesky(factor1_normed, damping1)
return jnp.full((1, 1), s), factor1_inv
if factor1.size == 1:
factor0_normed = factor0 / norm0
damping0 = d / norm0
factor0_inv = psd_inv_cholesky(factor0_normed, damping0)
return factor0_inv, jnp.full((1, 1), s)
# Invert first factor
factor0_normed = factor0 / norm0
damping0 = jnp.sqrt(d * factor1.shape[0] / (s * factor0.shape[0]))
factor0_inv = psd_inv_cholesky(factor0_normed, damping0) / jnp.sqrt(s)
# Invert second factor
factor1_normed = factor1 / norm1
damping1 = jnp.sqrt(d * factor0.shape[0] / (s * factor1.shape[0]))
factor1_inv = psd_inv_cholesky(factor1_normed, damping1) / jnp.sqrt(s)
return factor0_inv, factor1_inv
def zero_inverse(
operand: Sequence[jnp.ndarray]) -> Tuple[jnp.ndarray, jnp.ndarray]:
return (jnp.eye(factor_0.shape[0]) / jnp.sqrt(operand[-1]),
jnp.eye(factor_1.shape[0]) / jnp.sqrt(operand[-1]))
# In the special case where for some reason one of the factors is zero, then
# the correct inverse of `(0 kron A + lambda I)` is
# `(I/sqrt(lambda) kron (I/sqrt(lambda)`. However, because one of the norms is
# zero, then `pi` and `1/pi` would be 0 and infinity leading to NaN values.
# Hence, we need to make this check explicitly.
return lax.cond(jnp.greater(scale, 0.0),
regular_inverse,
zero_inverse,
operand=(factor_0, factor_1, norm_0, norm_1, scale,
damping))
def update(self, params, x, y, loss=None):
"""
Description: Updates parameters based on correct value, loss and learning rate.
Args:
params (list/numpy.ndarray): Parameters of method pred method
x (float): input to method
y (float): true label
loss (function): loss function. defaults to input value.
Returns:
Updated parameters in same shape as input
"""
assert self.initialized
assert type(
params
) == dict, "optimizers can only take params in dictionary format"
grad = self.gradient(params, x, y,
loss=loss) # defined in optimizers core class
if self.theta is None:
self.theta = {
k: -dw
for (k, w), dw in zip(params.items(), grad.values())
}
else:
self.theta = {
k: v - dw
for (k, v), dw in zip(self.theta.items(), grad.values())
}
if self.eta is None:
self.eta = {
k: dw * dw
for (k, w), dw in zip(params.items(), grad.values())
}
else:
self.eta = {
k: v + dw * dw
for (k, v), dw in zip(self.eta.items(), grad.values())
}
if self.theta_max is None:
self.theta_max = {
k: np.absolute(v)
for (k, v) in self.theta.items()
}
else:
self.theta_max = {
k: np.where(np.greater(np.absolute(v), v_max), np.absolute(v),
v_max)
for (k, v), v_max in zip(self.theta.items(),
self.theta_max.values())
}
new_params = {
k: np.where(np.equal(0.0, np.maximum(theta_max, eta)), theta,
theta / np.sqrt(np.maximum(theta_max, eta)))
for (k, w), theta, theta_max, eta in zip(params.items(
), self.theta.values(), self.theta_max.values(), self.eta.values())
}
x_new = np.roll(x, 1)
x_new = jax.ops.index_update(x_new, 0, y)
y_t = self.pred(params=new_params, x=x_new)
# print('y before {0}'.format(y_t))
x_plus_bias_new = np.vstack((np.ones((1, 1)), x_new))
new_mapped_params = {
k: self.norm_project(
np.where(np.equal(0.0, np.maximum(theta_max, eta)), 0.0,
1.0 / np.sqrt(np.maximum(theta_max, eta))),
x_plus_bias_new, y_t, p)
for (k, p), theta_max, eta in zip(
new_params.items(), self.theta_max.values(), self.eta.values())
}
# y_t = self.pred(params=new_mapped_params, x=x_new)
# print('y after {0}'.format(y_t))
return new_mapped_params
def fit(self,
λ,
ϵ,
rng=None,
patience=100,
min_iterations=100,
max_iterations=int(1e5),
print_rate=None,
best=True):
"""Fitting routine for the IMNN
Parameters
----------
λ : float
Coupling strength of the regularisation
ϵ : float
Closeness criterion describing how close to the 1 the determinant
of the covariance (and inverse covariance) of the network outputs
is desired to be
rng : int(2,) or None, default=None
Stateless random number generator
patience : int, default=10
Number of iterations where there is no increase in the value of the
determinant of the Fisher information matrix, used for early
stopping
min_iterations : int, default=100
Number of iterations that should be run before considering early
stopping using the patience counter
max_iterations : int, default=int(1e5)
Maximum number of iterations to run the fitting procedure for
print_rate : int or None, default=None,
Number of iterations before updating the progress bar whilst
fitting. There is a performance hit from updating the progress bar
more often and there is a large performance hit from using the
progress bar at all. (Possible ``RET_CHECK`` failure if
``print_rate`` is not ``None`` when using GPUs).
For this reason it is set to None as default
best : bool, default=True
Whether to set the network parameter attribute ``self.w`` to the
parameter values that obtained the maximum determinant of
the Fisher information matrix or the parameter values at the final
iteration of fitting
Example
-------
We are going to summarise the mean and variance of some random Gaussian
noise with 10 data points per example using an AggregatedSimulatorIMNN.
In this case we are going to generate the simulations on-the-fly with a
simulator written in jax (from the examples directory). These
simulations will be generated on-the-fly and passed through the network
on each of the GPUs in ``jax.devices("gpu")`` and we will make 100
simulations on each device at a time. The main computation will be done
on the CPU. We will use 1000 simulations to estimate the covariance of
the network outputs and the derivative of the mean of the network
outputs with respect to the model parameters (Gaussian mean and
variance) and generate the simulations at a fiducial μ=0 and Σ=1. The
network will be a stax model with hidden layers of ``[128, 128, 128]``
activated with leaky relu and outputting 2 summaries. Optimisation will
be via Adam with a step size of ``1e-3``. Rather arbitrarily we'll set
the regularisation strength and covariance identity constraint to λ=10
and ϵ=0.1 (these are relatively unimportant for such an easy model).
.. code-block:: python
import jax
import jax.numpy as np
from jax.experimental import stax, optimizers
from imnn import AggregatedSimulatorIMNN
rng = jax.random.PRNGKey(0)
n_s = 1000
n_d = 1000
n_params = 2
n_summaries = 2
input_shape = (10,)
θ_fid = np.array([0., 1.])
def simulator(rng, θ):
return θ[0] + jax.random.normal(
rng, shape=input_shape) * np.sqrt(θ[1])
model = stax.serial(
stax.Dense(128),
stax.LeakyRelu,
stax.Dense(128),
stax.LeakyRelu,
stax.Dense(128),
stax.LeakyRelu,
stax.Dense(n_summaries))
optimiser = optimizers.adam(step_size=1e-3)
λ = 10.
ϵ = 0.1
model_key, fit_key = jax.random.split(rng)
host = jax.devices("cpu")[0]
devices = jax.devices("gpu")
n_per_device = 100
imnn = AggregatedSimulatorIMNN(
n_s=n_s, n_d=n_d, n_params=n_params, n_summaries=n_summaries,
input_shape=input_shape, θ_fid=θ_fid, model=model,
optimiser=optimiser, key_or_state=model_key,
simulator=simulator, host=host, devices=devices,
n_per_device=n_per_device)
imnn.fit(λ, ϵ, rng=fit_key, min_iterations=1000, patience=250,
print_rate=None)
Notes
-----
A minimum number of interations should be be run before stopping based
on a maximum determinant of the Fisher information achieved since the
loss function has dual objectives. Since the determinant of the
covariance of the network outputs is forced to 1 quickly, this can be
at the detriment to the value of the determinant of the Fisher
information matrix early in the fitting procedure. For this reason
starting early stopping after the covariance has converged is advised.
This is not currently implemented but could be considered in the
future.
The best fit network parameter values are probably not the most
representative set of parameters when simulating on-the-fly since there
is a high chance of a statistically overly-informative set of data
being generated. Instead, if using
:func:`~imnn.AggregatedSimulatorIMNN.fit()` consider using
``best=False`` which sets ``self.w=self.final_w`` which are the network
parameter values obtained in the last iteration. Also consider using a
larger ``patience`` value if using :func:`~imnn.SimulatorIMNN.fit()`
to overcome the fact that a flukish high value for the determinant
might have been obtained due to the realisation of the dataset.
Raises
------
TypeError
If any input has the wrong type
ValueError
If any input (except ``rng``) are ``None``
ValueError
If ``rng`` has the wrong shape
ValueError
If ``rng`` is ``None`` but simulating on-the-fly
Methods
-------
get_keys_and_params:
Jitted collection of parameters and random numbers
calculate_loss:
Returns the jitted gradient of the loss function wrt summaries
validation_loss:
Jitted loss and auxillary statistics from validation set
Todo
----
- ``rng`` is currently only used for on-the-fly simulation but could
easily be updated to allow for stochastic models
- Automatic detection of convergence based on value ``r`` when early
stopping can be started
"""
@jax.jit
def get_keys_and_params(rng, state):
"""Jitted collection of parameters and random numbers
Parameters
----------
rng : int(2,) or None, default=None
Stateless random number generator
state : :obj:state
The optimiser state used for updating the network parameters
and optimisation algorithm
Returns
-------
int(2,) or None, default=None:
Stateless random number generator
int(2,) or None, default=None:
Stateless random number generator for training
int(2,) or None, default=None:
Stateless random number generator for validation
list:
Network parameter values
"""
rng, training_key, validation_key = self._get_fitting_keys(rng)
w = self._get_parameters(state)
return rng, training_key, validation_key, w
@jax.jit
@partial(jax.grad, argnums=(0, 1), has_aux=True)
def calculate_loss(summaries, summary_derivatives):
"""Returns the jitted gradient of the loss function wrt summaries
Used to calculate the gradient of the loss function wrt summaries
and derivatives of the summaries with respect to model parameters
which will be used to calculate the aggregated gradient of the
Fisher information with respect to the network parameters via the
chain rule.
Parameters
----------
summaries : float(n_s, n_summaries)
The network outputs
summary_derivatives : float(n_d, n_summaries, n_params)
The derivative of the network outputs wrt the model parameters
Returns
-------
tuple:
Gradient of the loss function with respect to network outputs
and their derivatives with respect to physical model parameters
tuple:
Fitting statistics calculated on a single iteration
- **F** *(float(n_params, n_params))* -- Fisher information
matrix
- **C** *(float(n_summaries, n_summaries))* -- covariance
of network outputs
- **invC** *(float(n_summaries, n_summaries))* -- inverse
covariance of network outputs
- **Λ2** *(float)* -- covariance regularisation
- **r** *(float)* -- regularisation coupling strength
"""
return self._calculate_loss(summaries, summary_derivatives, λ, α)
@jax.jit
def validation_loss(summaries, derivatives):
"""Jitted loss and auxillary statistics from validation set
Parameters
----------
summaries : float(n_s, n_summaries)
The network outputs
summary_derivatives : float(n_d, n_summaries, n_params)
The derivative of the network outputs wrt the model parameters
Returns
-------
tuple:
Fitting statistics calculated on a single validation iteration
- **F** *(float(n_params, n_params))* -- Fisher information
matrix
- **C** *(float(n_summaries, n_summaries))* -- covariance
of network outputs
- **invC** *(float(n_summaries, n_summaries))* -- inverse
covariance of network outputs
- **Λ2** *(float)* -- covariance regularisation
- **r** *(float)* -- regularisation coupling strength
"""
F, C, invC, *_ = self._calculate_F_statistics(
summaries, derivatives)
_Λ2 = self._get_regularisation(C, invC)
_r = self._get_regularisation_strength(_Λ2, λ, α)
return (F, C, invC, _Λ2, _r)
λ = _check_type(λ, float, "λ")
ϵ = _check_type(ϵ, float, "ϵ")
α = self.get_α(λ, ϵ)
patience = _check_type(patience, int, "patience")
min_iterations = _check_type(min_iterations, int, "min_iterations")
max_iterations = _check_type(max_iterations, int, "max_iterations")
best = _check_boolean(best, "best")
if self.simulate and (rng is None):
raise ValueError("`rng` is necessary when simulating.")
rng = _check_input(rng, (2, ), "rng", allow_None=True)
max_detF, best_w, detF, detC, detinvC, Λ2, r, counter, \
patience_counter, state, rng = self._set_inputs(
rng, max_iterations)
pbar, print_rate, remainder = self._setup_progress_bar(
print_rate, max_iterations)
while self._fit_cond((max_detF, best_w, detF, detC, detinvC, Λ2, r,
counter, patience_counter, state, rng),
patience=patience,
max_iterations=max_iterations):
rng, training_key, validation_key, w = get_keys_and_params(
rng, state)
summaries, summary_derivatives = self.get_summaries(
w=w, key=training_key)
dΛ_dx, results = calculate_loss(summaries, summary_derivatives)
grad = self.get_gradient(dΛ_dx, w, key=training_key)
state = self._update(counter, grad, state)
w = self._get_parameters(state)
detF, detC, detinvC, Λ2, r = self._update_history(
results, (detF, detC, detinvC, Λ2, r), counter, 0)
if self.validate:
summaries, summary_derivatives = self.get_summaries(
w=w, key=training_key, validate=True)
results = validation_loss(summaries, summary_derivatives)
detF, detC, detinvC, Λ2, r = self._update_history(
results, (detF, detC, detinvC, Λ2, r), counter, 1)
_detF = np.linalg.det(results[0])
patience_counter, counter, _, max_detF, __, best_w = \
jax.lax.cond(
np.greater(_detF, max_detF),
self._update_loop_vars,
lambda inputs: self._check_loop_vars(
inputs, min_iterations),
(patience_counter, counter, _detF, max_detF, w, best_w))
self._update_progress_bar(pbar, counter, patience_counter,
max_detF, detF[counter], detC[counter],
detinvC[counter], Λ2[counter],
r[counter], print_rate, max_iterations,
remainder)
counter += 1
self._update_progress_bar(pbar,
counter,
patience_counter,
max_detF,
detF[counter - 1],
detC[counter - 1],
detinvC[counter - 1],
Λ2[counter - 1],
r[counter - 1],
print_rate,
max_iterations,
remainder,
close=True)
self.history["max_detF"] = max_detF
self.best_w = best_w
self._set_history((detF[:counter], detC[:counter], detinvC[:counter],
Λ2[:counter], r[:counter]))
self.state = state
self.final_w = self._get_parameters(self.state)
if best:
w = self.best_w
else:
w = self.final_w
self.set_F_statistics(w, key=rng)
def greater(x1, x2): if isinstance(x1, JaxArray): x1 = x1.value if isinstance(x2, JaxArray): x2 = x2.value return JaxArray(jnp.greater(x1, x2))
def gt(a: Numeric, b: Numeric):
return jnp.greater(a, b)
def unstack(self, stacked):
"""Inverts stacking over time.
Given 'stacked' outputs from this StackingOverTime layer,
stacked, _ = this_layer.FProp(inputs),
this method attempts to reconstruct the original 'inputs'.
If stride > window_size, the original input cannot be recovered, and a
ValueError is raised.
Otherwise, if right_context + 1 >= stride, this method returns a Tensor that
is identical to 'inputs' but potentially longer due to paddings.
If right_context + 1 < stride, this method returns a Tensor that may be up
to ```stride - right_context - 1``` frames shorter than the original input,
but identical in the frames that are returned. e.g.::
left_context = 2, right_context = 1, stride = 4
input sequence: 1 2 3 4 5 6 7 8
after padding: 0 0 1 2 3 4 5 6 7 8 0
windows:
[0 0 (1) 2] 3 4 5 6 7 8 0
0 0 1 2 [3 4 (5) 6] 7 8 0
stacked:
[[0 0 1 2], [3 4 5 6]]
unstacked:
[1 2 3 4 5 6], which is 4 - 1 - 1 = 2 (stride - right_context - 1)
frames shorter than the original input.
`unstack()` can be used to project the outputs of downstream layers back to
the shape of the original unstacked inputs. For example::
inputs = ... # [batch, length, input_dim]
# [batch, ceil(length / stride), rnn_dim]
rnn_out = rnn.fprop(stacking.fprop(inputs)[0])
# [batch, length, rnn_dim]
back_projected_rnn_out = py_utils.PadOrTrimTo(
stacking.unstack(jnp.tile(rnn_out, [1, 1, stacking.window_size])),
inputs.shape)
Note this method does not take or return a separate padding JTensor. The
caller is responsible for knowing which of outputs are padding (e.g. based
on the padding of the original FProp inputs).
Args:
stacked: JTensor of shape [batch, time, window_size * feature_dim],
assumed to be the output of `fprop`.
Returns:
The reconstructed input JTensor, with shape
[batch, (frames - 1) * stride + right_context + 1, feature_dim].
Raises:
ValueError: if stride > window_size.
"""
p = self.params
if 0 == p.left_context == p.right_context and 1 == p.stride:
return stacked
if p.stride > self.window_size:
raise ValueError(
"Can't invert StackingOverTime with stride (%d) > window_size (%d)" %
(p.stride, self.window_size))
# Reshape to allow indexing individual frames within each stacked window.
batch_size, stacked_length, _ = stacked.shape
stacked = jnp.reshape(stacked,
[batch_size, stacked_length, self.window_size, -1])
# Compute the index of the window and frame in 'stacked' where each frame of
# the original input is located, and extract them with tf.gather_nd.
# First compute for all except the last window, since these elements have
# the potential of being looked up from the next window.
input_indices = jnp.arange(0, (stacked_length - 1) * p.stride)
mod = input_indices % p.stride
in_next_window = jnp.greater(mod, p.right_context).astype(jnp.int32)
window_index = input_indices // p.stride + in_next_window
frame_index = p.left_context + mod - p.stride * in_next_window
# Now handle the last window explicitly and concatenate onto the existing
# window_index/frame_index tensors.
last_window_length = p.right_context + 1
window_index = jnp.concatenate([
window_index,
jnp.repeat(jnp.array([stacked_length - 1]), last_window_length)
],
axis=0)
frame_index = jnp.concatenate(
[frame_index, p.left_context + jnp.arange(last_window_length)], axis=0)
# Stack the indices for gather_nd operation below
window_and_frame_indices = jnp.stack([window_index, frame_index], axis=1)
window_and_frame_indices = jnp.tile(
jnp.expand_dims(window_and_frame_indices, 0), [batch_size, 1, 1])
# jax equivalent of tf.gather_nd
def gather_nd_unbatched(params, indices):
return params[tuple(jnp.moveaxis(indices, -1, 0))]
return vmap(gather_nd_unbatched, (0, 0), 0)(stacked,
window_and_frame_indices)
lambda x, name=None: scipy_special.erf(x))
erfc = utils.copy_docstring(tf.math.erfc,
lambda x, name=None: scipy_special.erfc(x))
exp = utils.copy_docstring(tf.math.exp, lambda x, name=None: np.exp(x))
expm1 = utils.copy_docstring(tf.math.expm1, lambda x, name=None: np.expm1(x))
floor = utils.copy_docstring(tf.math.floor, lambda x, name=None: np.floor(x))
floordiv = utils.copy_docstring(tf.math.floordiv,
lambda x, y, name=None: np.floor_divide(x, y))
greater = utils.copy_docstring(tf.math.greater,
lambda x, y, name=None: np.greater(x, y))
greater_equal = utils.copy_docstring(
tf.math.greater_equal, lambda x, y, name=None: np.greater_equal(x, y))
igamma = utils.copy_docstring(
tf.math.igamma, lambda a, x, name=None: scipy_special.gammainc(a, x))
igammac = utils.copy_docstring(
tf.math.igammac, lambda a, x, name=None: scipy_special.gammaincc(a, x))
imag = utils.copy_docstring(tf.math.imag,
lambda input, name=None: np.imag(input))
# in_top_k = utils.copy_docstring(
# tf.math.in_top_k,
def w_cond(self, args):
_, loc, counter = args
return np.logical_and(
np.logical_or(np.any(np.greater(loc, self.high)),
np.any(np.less(loc, self.low))),
np.less(counter, self.max_counter))
def delete(m, i): n = m.shape[0] before_inds = np.arange(n-1)*np.less(np.arange(n-1),i) after_inds = (np.arange(n-1)+1)*np.greater(np.arange(n-1)+1,i) inds = before_inds + after_inds return m[inds]
def stable_exp(x):
"""If x is greater than thresh, use first order Taylor's expansion."""
return jnp.where(jnp.greater(x, thresh),
jnp.exp(thresh) * (1 + x - thresh), jnp.exp(x))
