"""
Pipeline for training and using a Flux1Joint model for simulation-based inference.
"""
import jax.numpy as jnp
from flax import nnx
from gensbi.models import (
Flux1Joint,
Flux1JointParams,
)
import yaml
from gensbi.recipes.joint_pipeline import JointPipeline
from gensbi.recipes.utils import parse_training_config
[docs]
def parse_flux1joint_params(config_path: str):
"""
Parse a Flux1Joint configuration file.
Parameters
----------
config_path : str
Path to the configuration file.
Returns
-------
config : dict
Parsed configuration dictionary.
"""
with open(config_path, "r") as f:
config = yaml.safe_load(f)
model_params = config.get("model", {})
params_dict = dict(
in_channels=model_params.get("in_channels", 1),
vec_in_dim=model_params.get("vec_in_dim", None),
mlp_ratio=model_params.get("mlp_ratio", 3.0),
num_heads=model_params.get("num_heads", 4),
depth_single_blocks=model_params.get("depth_single_blocks", 8),
val_emb_dim=model_params.get(
"val_emb_dim", model_params.get("val_emb_dim", 10)
), # Support both for now
cond_emb_dim=model_params.get("cond_emb_dim", 4),
id_emb_dim=model_params.get("id_emb_dim", 10),
qkv_bias=model_params.get("qkv_bias", True),
id_merge_mode=model_params.get(
"id_merge_mode", model_params.get("id_merge_mode", "concat")
),
id_embedding_strategy=model_params.get(
"id_embedding_strategy", model_params.get("id_embedding_strategy", "absolute")
),
guidance_embed=model_params.get("guidance_embed", False),
param_dtype=getattr(jnp, model_params.get("param_dtype", "float32")),
)
return params_dict
[docs]
def get_default_flux1joint_params(dim_joint: int, in_channels: int = 1):
"""
Return default parameters for the Flux1Joint model.
"""
return Flux1JointParams(
in_channels=in_channels,
vec_in_dim=None,
mlp_ratio=3.0,
num_heads=4,
depth_single_blocks=8,
val_emb_dim=10,
cond_emb_dim=4,
id_emb_dim=10,
qkv_bias=True,
rngs=nnx.Rngs(0),
dim_joint=dim_joint,
id_merge_mode="concat",
guidance_embed=False,
param_dtype=jnp.bfloat16,
)
[docs]
def _flux1joint_config_from_path(config_path: str, dim_joint: int):
"""
Helper to parse common configuration for Flux1Joint pipelines.
Returns
-------
params : Flux1JointParams
The parsed model parameters.
training_config : dict
The parsed training configuration.
method : str
The methodology (flow or diffusion) specified in the config.
"""
with open(config_path, "r") as f:
config = yaml.safe_load(f)
# methodology
strategy = config.get("strategy", {})
method = strategy.get("method")
model_type = strategy.get("model")
if model_type != "flux1joint":
raise ValueError(f"Model type {model_type} not supported.")
params_dict = parse_flux1joint_params(config_path)
params = Flux1JointParams(
rngs=nnx.Rngs(0),
dim_joint=dim_joint,
**params_dict,
)
training_config = parse_training_config(config_path)
return params, training_config, method
[docs]
class Flux1JointFlowPipeline(JointPipeline):
def __init__(
self,
train_dataset,
val_dataset,
dim_obs: int,
dim_cond: int,
ch_obs: int = 1,
params=None,
training_config=None,
condition_mask_kind="structured",
):
"""
Flow pipeline for training and using a Flux1Joint model for simulation-based inference.
Parameters
----------
train_dataset : grain dataset or iterator over batches
Training dataset.
val_dataset : grain dataset or iterator over batches
Validation dataset.
dim_obs : int
Dimension of the parameter space.
dim_cond : int
Dimension of the observation space.
ch_obs : int
Number of channels in the observation data.
params : Flux1JointParams, optional
Parameters for the Flux1Joint model. If None, default parameters are used.
training_config : dict, optional
Configuration for training. If None, default configuration is used.
condition_mask_kind : str, optional
Kind of condition mask to use. One of ["structured", "posterior"].
Examples
--------
Minimal example on how to instantiate and use the Flux1JointFlowPipeline:
.. literalinclude:: /examples/flux1joint_flow_pipeline.py
:language: python
:linenos:
.. image:: /examples/flux1joint_flow_pipeline_marginals.png
:width: 600
.. note::
If you plan on using multiprocessing prefetching, ensure that your script is wrapped
in a ``if __name__ == "__main__":`` guard.
See https://docs.python.org/3/library/multiprocessing.html
"""
[docs]
self.dim_joint = dim_obs + dim_cond
if params is None:
params = get_default_flux1joint_params(self.dim_joint, self.ch_obs)
model = self._make_model(params)
from gensbi.core import FlowMatchingMethod
super().__init__(
model=model,
train_dataset=train_dataset,
val_dataset=val_dataset,
dim_obs=dim_obs,
dim_cond=dim_cond,
method=FlowMatchingMethod(),
ch_obs=ch_obs,
params=params,
training_config=training_config,
condition_mask_kind=condition_mask_kind,
)
[docs]
self.ema_model = nnx.clone(self.model)
@classmethod
[docs]
def init_pipeline_from_config(
cls,
train_dataset,
val_dataset,
dim_obs: int,
dim_cond: int,
config_path: str,
checkpoint_dir: str,
**kwargs,
):
"""
Initialize the pipeline from a configuration file.
Parameters
----------
config_path : str
Path to the configuration file.
**kwargs
Additional keyword arguments forwarded to the constructor.
"""
params, training_config, method = _flux1joint_config_from_path(
config_path, dim_obs + dim_cond
)
if method != "flow":
raise ValueError(
f"Method {method} not supported in Flux1JointFlowPipeline."
)
# add checkpoint dir to training config
training_config["checkpoint_dir"] = checkpoint_dir
pipeline = cls(
train_dataset,
val_dataset,
dim_obs,
dim_cond,
ch_obs=params.in_channels,
params=params,
training_config=training_config,
**kwargs,
)
return pipeline
[docs]
def _make_model(self, params):
"""
Create and return the Flux1Joint model to be trained.
"""
model = Flux1Joint(params)
return model
@classmethod
[docs]
def get_default_params(cls, dim_joint, in_channels):
"""
Return a dictionary of default model parameters.
"""
return get_default_flux1joint_params(dim_joint, in_channels)
[docs]
class Flux1JointSMPipeline(JointPipeline):
def __init__(
self,
train_dataset,
val_dataset,
dim_obs: int,
dim_cond: int,
ch_obs: int = 1,
sde_type: str = "VP",
params=None,
training_config=None,
condition_mask_kind="structured",
):
"""
Score matching pipeline for training and using a Flux1Joint model for simulation-based inference.
Parameters
----------
train_dataset : grain dataset or iterator over batches
Training dataset.
val_dataset : grain dataset or iterator over batches
Validation dataset.
dim_obs : int
Dimension of the parameter space.
dim_cond : int
Dimension of the observation space.
ch_obs : int
Number of channels in the observation data.
sde_type : str
Type of SDE. One of ``"VP"`` or ``"VE"``.
params : Flux1JointParams, optional
Parameters for the Flux1Joint model. If None, default parameters are used.
training_config : dict, optional
Configuration for training. If None, default configuration is used.
condition_mask_kind : str, optional
Kind of condition mask to use. One of ["structured", "posterior"].
"""
[docs]
self.dim_joint = dim_obs + dim_cond
if params is None:
params = get_default_flux1joint_params(self.dim_joint, self.ch_obs)
model = self._make_model(params)
from gensbi.core import ScoreMatchingMethod
super().__init__(
model=model,
train_dataset=train_dataset,
val_dataset=val_dataset,
dim_obs=dim_obs,
dim_cond=dim_cond,
method=ScoreMatchingMethod(sde_type=sde_type),
ch_obs=ch_obs,
params=params,
training_config=training_config,
condition_mask_kind=condition_mask_kind,
)
[docs]
self.ema_model = nnx.clone(self.model)
@classmethod
[docs]
def init_pipeline_from_config(
cls,
train_dataset,
val_dataset,
dim_obs: int,
dim_cond: int,
config_path: str,
checkpoint_dir: str,
**kwargs,
):
"""
Initialize the pipeline from a configuration file.
Parameters
----------
config_path : str
Path to the configuration file.
**kwargs
Additional keyword arguments forwarded to the constructor
(e.g. ``sde_type="VE"`` for score matching).
"""
params, training_config, method = _flux1joint_config_from_path(
config_path, dim_obs + dim_cond
)
if method != "score_matching":
raise ValueError(
f"Method {method} not supported in Flux1JointSMPipeline."
)
training_config["checkpoint_dir"] = checkpoint_dir
pipeline = cls(
train_dataset,
val_dataset,
dim_obs,
dim_cond,
ch_obs=params.in_channels,
params=params,
training_config=training_config,
**kwargs,
)
return pipeline
[docs]
def _make_model(self, params):
"""
Create and return the Flux1Joint model to be trained.
"""
model = Flux1Joint(params)
return model
@classmethod
[docs]
def get_default_params(cls, dim_joint, in_channels):
"""
Return a dictionary of default model parameters.
"""
return get_default_flux1joint_params(dim_joint, in_channels)
[docs]
class Flux1JointDiffusionPipeline(JointPipeline):
def __init__(
self,
train_dataset,
val_dataset,
dim_obs: int,
dim_cond: int,
ch_obs: int = 1,
params=None,
training_config=None,
condition_mask_kind="structured",
):
"""
Diffusion pipeline for training and using a Flux1Joint model for simulation-based inference.
Parameters
----------
train_dataset : grain dataset or iterator over batches
Training dataset.
val_dataset : grain dataset or iterator over batches
Validation dataset.
dim_obs : int
Dimension of the parameter space.
dim_cond : int
Dimension of the observation space.
ch_obs : int
Number of channels in the observation data.
params : Flux1JointParams, optional
Parameters for the Flux1Joint model. If None, default parameters are used.
training_config : dict, optional
Configuration for training. If None, default configuration is used.
condition_mask_kind : str, optional
Kind of condition mask to use. One of ["structured", "posterior"]. Default is "structured".
Examples
--------
Minimal example on how to instantiate and use the Flux1JointDiffusionPipeline:
.. literalinclude:: /examples/flux1joint_diffusion_pipeline.py
:language: python
:linenos:
.. image:: /examples/flux1joint_diffusion_pipeline_marginals.png
:width: 600
.. note::
If you plan on using multiprocessing prefetching, ensure that your script is wrapped
in a ``if __name__ == "__main__":`` guard.
See https://docs.python.org/3/library/multiprocessing.html
"""
[docs]
self.dim_joint = dim_obs + dim_cond
if params is None:
params = get_default_flux1joint_params(self.dim_joint, self.ch_obs)
model = self._make_model(params)
from gensbi.core import DiffusionEDMMethod
super().__init__(
model=model,
train_dataset=train_dataset,
val_dataset=val_dataset,
dim_obs=dim_obs,
dim_cond=dim_cond,
method=DiffusionEDMMethod(),
ch_obs=ch_obs,
params=params,
training_config=training_config,
condition_mask_kind=condition_mask_kind,
)
[docs]
self.ema_model = nnx.clone(self.model)
@classmethod
[docs]
def init_pipeline_from_config(
cls,
train_dataset,
val_dataset,
dim_obs: int,
dim_cond: int,
config_path: str,
checkpoint_dir: str,
**kwargs,
):
"""
Initialize the pipeline from a configuration file.
Parameters
----------
config_path : str
Path to the configuration file.
**kwargs
Additional keyword arguments forwarded to the constructor.
"""
params, training_config, method = _flux1joint_config_from_path(
config_path, dim_obs + dim_cond
)
if method != "diffusion":
raise ValueError(
f"Method {method} not supported in Flux1JointDiffusionPipeline."
)
# add checkpoint dir to training config
training_config["checkpoint_dir"] = checkpoint_dir
pipeline = cls(
train_dataset,
val_dataset,
dim_obs,
dim_cond,
ch_obs=params.in_channels,
params=params,
training_config=training_config,
**kwargs,
)
return pipeline
[docs]
def _make_model(self, params):
"""
Create and return the Flux1Joint model to be trained.
"""
model = Flux1Joint(params)
return model
@classmethod
[docs]
def get_default_params(cls, dim_joint, in_channels):
"""
Return a dictionary of default model parameters.
"""
return get_default_flux1joint_params(dim_joint, in_channels)
