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# -*- coding: utf-8 -*-
"""
Created on Fri Aug 9 15:52:59 2024
about:
=======
ModuleBase: gives each model the sow_flax_intermeds and summary_stats
helpers, for tensorboard writing
neuralTKFModuleBase: adds functions for automatically applying key
activations: bound_sigmoid and log_softmax
SeqEmbBase: inherits ModuleBase and adds extra helpers for sequence embedding
applying encoder and decoder in training/eval; the following
models will need newer versions (and why):
- LSTM (uses "datalens" in argument list)
- Transformer (handle "output attn weights" argument)
- if you ever want to implement BatchNorm, rage quit and
migrate to flax.NNX (jk)
"""
from flax import linen as nn
import jax
import jax.numpy as jnp
import optax
from neural_models.neural_hmm_predict.model_functions import bound_sigmoid
from typing import Callable, Literal
from numpy.typing import ArrayLike
class ModuleBase(nn.Module):
def summary_stats(self,
mat: ArrayLike,
key_prefix: str,
include_min_max: bool = False,
include_perc_zeros: bool = False
):
"""
extract metrics from matrix
NOTE: metrics could be skewed with many zeros
arguments
---------
mat : ArrayLike
the matrix of interest
key_prefix : str
the variable name
include_min_max, include per_zeros : bool
if true, also include max, min, and percent zeros
returns
-------
out_dict : dict
dictionary containing summary stats
"""
if mat.size == 1:
out_dict = {f'{key_prefix}': jnp.squeeze(mat)}
else:
# always include: L2 norm, mean, variance
l2_norm = jnp.linalg.norm(mat.reshape(-1), ord=2)
mean = mat.mean()
variance = mat.var()
out_dict = {f'{key_prefix}/L2_NORM': l2_norm,
f'{key_prefix}/MEAN': mean,
f'{key_prefix}/VAR': variance}
# optional inclusions
if include_min_max:
out_dict[f'{key_prefix}/MAX'] = mat.max()
out_dict[f'{key_prefix}/MIN'] = mat.min()
if include_perc_zeros:
out_dict[f'{key_prefix}/PERC_ZEROS'] = (mat==0).sum() / mat.size
return out_dict
def sow_flax_intermeds(self,
mat: ArrayLike,
label: str,
include_min_max: bool = False,
include_perc_zeros: bool = False):
"""
helper to sow intermediate values
arguments
---------
mat : ArrayLike
the matrix of interest
label : str
the variable name
"""
# summarize
out_dict = self.summary_stats(mat=mat,
key_prefix=label,
include_min_max=include_min_max,
include_perc_zeros=include_perc_zeros)
# sow; only keep the most recent value
for name, value in out_dict.items():
self.sow(col = "sowed_intermeds",
name = name,
value = value,
reduce_fn = lambda a, b: b)
def maybe_sow(self,
sow_flax_intermeds: bool,
vals: ArrayLike,
label: str,
include_min_max: bool = False,
include_perc_zeros: bool = False):
"""
sow_flax_intermeds : bool
do this function or not
vals : ArrayLike
values to summarize and record
label : str
parameter name
include_min_max, include_perc_zeros : bool
include min, max, and zeros (I don't always need to do this)
"""
if sow_flax_intermeds:
self.sow_flax_intermeds(mat=vals,
label=label,
include_min_max=include_min_max,
include_perc_zeros=include_perc_zeros)
class neuralTKFModuleBase(ModuleBase):
"""
base class for neural TKF / neural HMM models
methods:
---------
maybe_sow : a wrapper around sow_flax_intermeds
apply_bound_sigmoid_activation : a wrapper around bound_sigmoid
apply_log_softmax_activation : a wrapper around log_softmax
inherited from ModuleBase:
--------------------------
maybe_sow
sow_flax_intermeds
summary_stats
"""
def apply_bound_sigmoid_activation(self,
logits: ArrayLike,
min_val: float,
max_val: float,
param_name: str,
sow_flax_intermeds: bool,
include_min_max: bool = False,
include_perc_zeros: bool = False):
"""
sigmoid(x) = 1 / ( 1 + exp(-x) )
bound_sigmoid(x, min, max) = min + ( ( max - min ) / ( 1 + exp(-x) ) )
"""
params = bound_sigmoid(logits, min_val, max_val)
self.maybe_sow( vals = params,
label = f'{self.name}/{param_name}',
sow_flax_intermeds = sow_flax_intermeds,
include_min_max=include_min_max,
include_perc_zeros=include_perc_zeros )
return params
def apply_log_softmax_activation(self,
logits: ArrayLike,
param_name: str,
sow_flax_intermeds: bool,
include_min_max: bool = False,
include_perc_zeros: bool = False):
"""
log_softmax(x) = log( softmax(x) )
"""
params = nn.log_softmax( logits, axis = -1 )
self.maybe_sow( vals = params,
label = f'{self.name}/{param_name}',
sow_flax_intermeds = sow_flax_intermeds,
include_min_max=include_min_max,
include_perc_zeros=include_perc_zeros )
return params
class SeqEmbBase(ModuleBase):
"""
base class for neural sequence embedding models
methods:
---------
apply_seq_embedder_in_training :
apply model during training
update_seq_embedder_tstate :
update parameters based on gradients
apply_seq_embedder_in_eval :
apply model during training
inherited from ModuleBase:
--------------------------
maybe_sow
sow_flax_intermeds
summary_stats
"""
def apply_seq_embedder_in_training(self,
seqs: ArrayLike,
tstate,
rng_key,
params_for_apply: dict,
sow_flax_intermeds: bool,
*args,
**kwargs):
"""
apply model during training
arguments
----------
seqs : ArrayLike
inputs for function
tstate : Flax.Trainstate
trainstate for sequence embedder
rng_key : Jax rng (whatever type that is)
rng key if needed (for example: for dropout)
params_for_apply : dict
parameters for trainstate object
sow_flax_intermeds : bool
whether or not to record intermediates, broadly
never record min, max, or percent zeros for these
returns
-------
out_embeddings : ArrayLike
per-position sequence embeddings
aux_data : dict
contains output, weight, and gradient from flax.sow, as well as
any additional values needed for embedding the ancestor
"""
# embed the sequence
out_embeddings, out_aux_dict = tstate.apply_fn(variables = params_for_apply,
datamat = seqs,
training = True,
sow_flax_intermeds = sow_flax_intermeds,
mutable = ["sowed_intermeds"] if sow_flax_intermeds else [],
rngs={'dropout': rng_key})
# pack up all the auxilary data
metrics_dict_name = f'{self.embedding_which}_layer_intermediates'
aux_data = {f'{metrics_dict_name}/sowed_intermeds' : out_aux_dict.get("sowed_intermeds", dict()) }
# if you ever use batch norm in ancestor sequence embedder, need
# to replace this whole method and extract batch_stats from out_aux_dict
if self.embedding_which == 'anc':
aux_data['anc_aux'] = None
return (out_embeddings, aux_data)
def update_seq_embedder_tstate(self,
tstate,
new_opt_state,
optim_updates,
*args,
**kwargs):
"""
If you apply batch norm ever, you'll need a new one of these
arguments
----------
tstate : Flax.Trainstate
trainstate for sequence embedder
new_opt_state : optimizer to overwrite
optim_updates : updates to apply
returns
--------
new_tstate : Flax.Trainstate
updated trainstate
"""
new_params = optax.apply_updates(tstate.params,
optim_updates)
new_tstate = tstate.replace(params = new_params,
opt_state = new_opt_state)
return new_tstate
def apply_seq_embedder_in_eval(self,
seqs: ArrayLike,
tstate,
sow_flax_intermeds: bool,
*args,
**kwargs):
"""
apply model during eval steps
arguments
----------
seqs : ArrayLike
inputs for function
tstate : Flax.Trainstate
trainstate for sequence embedder
sow_flax_intermeds : bool
whether or not to record intermediates, broadly
never record min, max, or percent zeros for these
returns
-------
out_embeddings : ArrayLike
per-position sequence embeddings
aux_data : dict
contains output, weight, and gradient from flax.sow, as well as
any additional values needed for embedding the ancestor
"""
# embed the sequence
out_embeddings, out_aux_dict = tstate.apply_fn(variables = tstate.params,
datamat = seqs,
training = False,
sow_flax_intermeds = sow_flax_intermeds,
mutable = ["sowed_intermeds"] if sow_flax_intermeds else [])
# pack up all the auxilary data
metrics_dict_name = f'{self.embedding_which}_layer_intermediates'
aux_data = {f'{metrics_dict_name}/sowed_intermeds' : out_aux_dict.get("sowed_intermeds", dict()) }
# if you ever use batch norm in ancestor sequence embedder, need
# to replace this whole method and extract batch_stats from out_aux_dict
if self.embedding_which == 'anc':
aux_data['anc_aux'] = None
return (out_embeddings, aux_data)
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