"""
Core model handling routines.
"""
from __future__ import print_function
__all__ = [
"list_models", "load_model", "load_model_info",
"build_model", "precompile_dlls", "reparameterize",
]
import os
from os.path import basename, join as joinpath
from glob import glob
import re
import copy
import numpy as np # type: ignore
# NOTE: delay loading of kernelcl, kernelcuda, kerneldll and kernelpy
# cl and cuda in particular take awhile since they try to establish a
# connection with the card to verify that the environment works.
from . import generate
from . import modelinfo
from . import product
from . import mixture
from . import custom
# pylint: disable=unused-import
try:
from typing import List, Union, Optional, Any, Tuple
from .kernel import KernelModel
from .modelinfo import ModelInfo
except ImportError:
pass
# pylint: enable=unused-import
CUSTOM_MODEL_PATH = os.environ.get('SAS_MODELPATH', "")
if CUSTOM_MODEL_PATH == "":
CUSTOM_MODEL_PATH = joinpath(os.path.expanduser("~"), ".sasmodels", "custom_models")
#if not os.path.isdir(CUSTOM_MODEL_PATH):
# os.makedirs(CUSTOM_MODEL_PATH)
# TODO: refactor composite model support
# The current load_model_info/build_model does not reuse existing model
# definitions when loading a composite model, instead reloading and
# rebuilding the kernel for each component model in the expression. This
# is fine in a scripting environment where the model is built when the script
# starts and is thrown away when the script ends, but may not be the best
# solution in a long-lived application. This affects the following functions:
#
# load_model
# load_model_info
# build_model
KINDS = ("all", "py", "c", "double", "single", "opencl", "1d", "2d",
"nonmagnetic", "magnetic")
[docs]def list_models(kind=None):
# type: (str) -> List[str]
"""
Return the list of available models on the model path.
*kind* can be one of the following:
* all: all models
* py: python models only
* c: c models only
* single: c models which support single precision
* double: c models which require double precision
* opencl: c models which run in opencl
* dll: c models which do not run in opencl
* 1d: models without orientation
* 2d: models with orientation
* magnetic: models supporting magnetic sld
* nommagnetic: models without magnetic parameter
For multiple conditions, combine with plus. For example, *c+single+2d*
would return all oriented models implemented in C which can be computed
accurately with single precision arithmetic.
"""
if kind and any(k not in KINDS for k in kind.split('+')):
raise ValueError("kind not in " + ", ".join(KINDS))
files = sorted(glob(joinpath(generate.MODEL_PATH, "[a-zA-Z]*.py")))
available_models = [basename(f)[:-3] for f in files]
if kind and '+' in kind:
all_kinds = kind.split('+')
condition = lambda name: all(_matches(name, k) for k in all_kinds)
else:
condition = lambda name: _matches(name, kind)
selected = [name for name in available_models if condition(name)]
return selected
[docs]def _matches(name, kind):
if kind is None or kind == "all":
return True
info = load_model_info(name)
pars = info.parameters.kernel_parameters
# TODO: may be adding Fq to the list at some point
is_pure_py = callable(info.Iq)
if kind == "py":
return is_pure_py
elif kind == "c":
return not is_pure_py
elif kind == "double":
return not info.single and not is_pure_py
elif kind == "single":
return info.single and not is_pure_py
elif kind == "opencl":
return info.opencl
elif kind == "dll":
return not info.opencl and not is_pure_py
elif kind == "2d":
return any(p.type == 'orientation' for p in pars)
elif kind == "1d":
return all(p.type != 'orientation' for p in pars)
elif kind == "magnetic":
return any(p.type == 'sld' for p in pars)
elif kind == "nonmagnetic":
return not any(p.type == 'sld' for p in pars)
return False
[docs]def load_model(model_name, dtype=None, platform='ocl'):
# type: (str, str, str) -> KernelModel
"""
Load model info and build model.
*model_name* is the name of the model, or perhaps a model expression
such as sphere*hardsphere or sphere+cylinder.
*dtype* and *platform* are given by :func:`build_model`.
"""
return build_model(load_model_info(model_name),
dtype=dtype, platform=platform)
[docs]def load_model_info(model_string):
# type: (str) -> modelinfo.ModelInfo
"""
Load a model definition given the model name.
*model_string* is the name of the model, or perhaps a model expression
such as sphere*cylinder or sphere+cylinder. Use '@' for a structure
factor product, e.g. sphere@hardsphere. Custom models can be specified by
prefixing the model name with 'custom.', e.g. 'custom.MyModel+sphere'.
This returns a handle to the module defining the model. This can be
used with functions in generate to build the docs or extract model info.
"""
if "+" in model_string:
parts = [load_model_info(part)
for part in model_string.split("+")]
return mixture.make_mixture_info(parts, operation='+')
elif "*" in model_string:
parts = [load_model_info(part)
for part in model_string.split("*")]
return mixture.make_mixture_info(parts, operation='*')
elif "@" in model_string:
p_info, q_info = [load_model_info(part)
for part in model_string.split("@")]
return product.make_product_info(p_info, q_info)
# We are now dealing with a pure model
elif "custom." in model_string:
pattern = "custom.([A-Za-z0-9_-]+)"
result = re.match(pattern, model_string)
if result is None:
raise ValueError("Model name in invalid format: " + model_string)
model_name = result.group(1)
# Use ModelName to find the path to the custom model file
model_path = joinpath(CUSTOM_MODEL_PATH, model_name + ".py")
if not os.path.isfile(model_path):
raise ValueError("The model file {} doesn't exist".format(model_path))
kernel_module = custom.load_custom_kernel_module(model_path)
return modelinfo.make_model_info(kernel_module)
kernel_module = generate.load_kernel_module(model_string)
return modelinfo.make_model_info(kernel_module)
_REPARAMETERIZE_DOCS = """\
Definition
----------
Constrain :ref:`%(base)s` according to the following::
%(translation)s
"""
_LHS_RE = re.compile(r"^ *(?<![.0-9])([A-Za-z_][A-Za-z0-9_]+) *=",
flags=re.MULTILINE)
[docs]def reparameterize(
base, parameters, translation, filename=None,
title=None, insert_after=None, docs=None, name=None,
source=None,
):
"""
Reparameterize an existing model.
*base* is the original modelinfo. This cannot be a reparameterized model;
only one level of reparameterization is supported.
*parameters* are the new parameter definitions that will be
included in the model info.
*translation* is a string each line containing *var = expr*. The variable
*var* can be a new intermediate value, or it can be a parameter from
the base model that will be replace by the expression. The expression
*expr* can be any C99 expression, including C-style if-expressions
*condition ? value1 : value2*. Expressions can use any new or existing
parameter that is not being replaced including intermediate values that
are previously defined. Parameters can only be assigned once, never
updated. C99 math functions are available, as well as any functions
defined in the base model or included in *source* (see below).
*filename* is the filename for the replacement model. This is usually
*__file__*, giving the path to the model file, but it could also be a
nominal filename for translations defined on-the-fly.
*title* is the model title, which defaults to *base.title* plus
" (reparameterized)".
*insert_after* controls parameter placement. By default, the new
parameters replace the old parameters in their original position.
Instead, you can provide a dictionary *{'par': 'newpar1,newpar2'}*
indicating that new parameters named *newpar1* and *newpar2* should
be included in the table after the existing parameter *par*, or at
the beginning if *par* is the empty string.
*docs* constains the doc string for the translated model, which by default
references the base model and gives the *translation* text.
*name* is the model name (default = :code:`"constrained_" + base.name`).
*source* is a list any additional C source files that should be included to
define functions and constants used in the translation expressions. This
will be included after all sources for the base model. Sources will only
be included once, even if they are listed in both places, so feel free to
list all dependencies for the helper function, such as "lib/polevl.c".
"""
if not isinstance(base, modelinfo.ModelInfo):
base = load_model_info(base)
if name is None:
name = filename if filename is not None else "constrained_" + base.name
name = os.path.basename(name).split('.')[0]
if title is None:
title = base.title + " (reparameterized)"
if docs is None:
lines = "\n ".join(s.lstrip() for s in translation.split('\n'))
docs = _REPARAMETERIZE_DOCS%{'base': base.id, 'translation': lines}
#source = merge_deps(base.source, source)
source = (base.source + [f for f in source if f not in base.source]
if source else base.source)
# TODO: don't repeat code from generate._build_translation
base_pars = [par.id for par in base.parameters.kernel_parameters]
old_pars = [match.group(1) for match in _LHS_RE.finditer(translation)
if match.group(1) in base_pars]
new_pars = [modelinfo.parse_parameter(*p) for p in parameters]
table = modelinfo.derive_table(base.parameters, remove=old_pars,
insert=new_pars, insert_after=insert_after)
caller = copy.copy(base)
caller.translation = translation
caller.name = caller.id = name
caller.docs = docs
caller.filename = filename
caller.parameters = table
caller.source = source
return caller
# Note: not used at the moment.
[docs]def merge_deps(old, new):
"""
Merge two dependency lists. The lists are partially ordered, with
all dependents coming after the items they depend on, but otherwise
order doesn't matter. The merged list preserves the partial ordering.
So if old and new both include the item "c", then all items that come
before "c" in old and new will come before "c" in the result, and all
items that come after "c" in old and new will come after "c" in the
result.
"""
if new is None:
return old
result = []
for item in new:
try:
index = old.index(item)
#print(item,"found in",old,"at",index,"giving",old[:index])
result.extend(old[:index])
old = old[index+1:]
except ValueError:
#print(item, "not found in", old)
pass
result.append(item)
#print("after", item, "old", old, "result", result)
result.extend(old)
return result
[docs]def build_model(model_info, dtype=None, platform="ocl"):
# type: (ModelInfo, str, str) -> KernelModel
"""
Prepare the model for the default execution platform.
This will return an OpenCL model, a DLL model or a python model depending
on the model and the computing platform.
*model_info* is the model definition structure returned from
:func:`load_model_info`.
*dtype* indicates whether the model should use single or double precision
for the calculation. Choices are 'single', 'double', 'quad', 'half',
or 'fast'. If *dtype* ends with '!', then force the use of the DLL rather
than OpenCL for the calculation.
*platform* should be "dll" to force the dll to be used for C models,
otherwise it uses the default "ocl".
"""
composition = model_info.composition
if composition is not None:
composition_type, parts = composition
models = [build_model(p, dtype=dtype, platform=platform) for p in parts]
if composition_type == 'mixture':
return mixture.MixtureModel(model_info, models)
elif composition_type == 'product':
P, S = models
return product.ProductModel(model_info, P, S)
else:
raise ValueError('unknown mixture type %s'%composition_type)
# If it is a python model, return it immediately
if callable(model_info.Iq):
from . import kernelpy
return kernelpy.PyModel(model_info)
numpy_dtype, fast, platform = parse_dtype(model_info, dtype, platform)
source = generate.make_source(model_info)
if platform == "dll":
from . import kerneldll
#print("building dll", numpy_dtype)
return kerneldll.load_dll(source['dll'], model_info, numpy_dtype)
elif platform == "cuda":
from . import kernelcuda
return kernelcuda.GpuModel(source, model_info, numpy_dtype, fast=fast)
else:
from . import kernelcl
#print("building ocl", numpy_dtype)
return kernelcl.GpuModel(source, model_info, numpy_dtype, fast=fast)
[docs]def precompile_dlls(path, dtype="double"):
# type: (str, str) -> List[str]
"""
Precompile the dlls for all builtin models, returning a list of dll paths.
*path* is the directory in which to save the dlls. It will be created if
it does not already exist.
This can be used when build the windows distribution of sasmodels
which may be missing the OpenCL driver and the dll compiler.
"""
from . import kerneldll
numpy_dtype = np.dtype(dtype)
if not os.path.exists(path):
os.makedirs(path)
compiled_dlls = []
for model_name in list_models():
model_info = load_model_info(model_name)
if not callable(model_info.Iq):
source = generate.make_source(model_info)['dll']
old_path = kerneldll.SAS_DLL_PATH
try:
kerneldll.SAS_DLL_PATH = path
dll = kerneldll.make_dll(source, model_info, dtype=numpy_dtype)
finally:
kerneldll.SAS_DLL_PATH = old_path
compiled_dlls.append(dll)
return compiled_dlls
[docs]def parse_dtype(model_info, dtype=None, platform=None):
# type: (ModelInfo, str, str) -> Tuple[np.dtype, bool, str]
"""
Interpret dtype string, returning np.dtype, fast flag and platform.
Possible types include 'half', 'single', 'double' and 'quad'. If the
type is 'fast', then this is equivalent to dtype 'single' but using
fast native functions rather than those with the precision level
guaranteed by the OpenCL standard. 'default' will choose the appropriate
default for the model and platform.
Platform preference can be specfied ("ocl", "cuda", "dll"), with the
default being OpenCL or CUDA if available, otherwise DLL. If the dtype
name ends with '!' then platform is forced to be DLL rather than GPU.
The default platform is set by the environment variable SAS_OPENCL,
SAS_OPENCL=driver:device for OpenCL, SAS_OPENCL=cuda:device for CUDA
or SAS_OPENCL=none for DLL.
This routine ignores the preferences within the model definition. This
is by design. It allows us to test models in single precision even when
we have flagged them as requiring double precision so we can easily check
the performance on different platforms without having to change the model
definition.
"""
# Assign default platform, overriding ocl with dll if OpenCL is unavailable
# If opencl=False OpenCL is switched off
if platform is None:
platform = "ocl"
# Check if type indicates dll regardless of which platform is given
if dtype is not None and dtype.endswith('!'):
platform = "dll"
dtype = dtype[:-1]
# Make sure model allows opencl/gpu
if not model_info.opencl:
platform = "dll"
# Make sure opencl is available, or fallback to cuda then to dll
if platform == "ocl":
from . import kernelcl
if not kernelcl.use_opencl():
from . import kernelcuda
platform = "cuda" if kernelcuda.use_cuda() else "dll"
# Convert special type names "half", "fast", and "quad"
fast = (dtype == "fast")
if fast:
dtype = "single"
elif dtype == "quad":
dtype = "longdouble"
elif dtype == "half":
dtype = "float16"
# Convert dtype string to numpy dtype. Use single precision for GPU
# if model allows it, otherwise use double precision.
if dtype is None or dtype == "default":
numpy_dtype = (generate.F32 if model_info.single and platform in ("ocl", "cuda")
else generate.F64)
else:
numpy_dtype = np.dtype(dtype)
# Make sure that the type is supported by GPU, otherwise use dll
if platform == "ocl":
from . import kernelcl
env = kernelcl.environment()
elif platform == "cuda":
from . import kernelcuda
env = kernelcuda.environment()
else:
env = None
if env is not None and not env.has_type(numpy_dtype):
platform = "dll"
if dtype is None:
numpy_dtype = generate.F64
return numpy_dtype, fast, platform
[docs]def test_composite_order():
"""
Check that mixture models produce the same result independent of ordder.
"""
def test_models(fst, snd):
"""Confirm that two models produce the same parameters"""
fst = load_model(fst)
snd = load_model(snd)
# Un-disambiguate parameter names so that we can check if the same
# parameters are in a pair of composite models. Since each parameter in
# the mixture model is tagged as e.g., A_sld, we ought to use a
# regex subsitution s/^[A-Z]+_/_/, but removing all uppercase letters
# is good enough.
# TODO: check that the models produce the same results
# Note that compare.py will give a misleading answer. For
# "cylinder+sphere" the A_radius parameter will use the default
# cylinder radius, but for "sphere+cylinder" it will use the default
# sphere radius so a simple comparison of the two will appear to be
# different unless you explicitly set radius, solvent, and solvent_sld
# for the A and B models.
fst = [[x for x in p.name if x == x.lower()]
for p in fst.info.parameters.kernel_parameters]
snd = [[x for x in p.name if x == x.lower()]
for p in snd.info.parameters.kernel_parameters]
assert sorted(fst) == sorted(snd), "{} != {}".format(fst, snd)
test_models(
"cylinder+sphere",
"sphere+cylinder")
test_models(
"cylinder*sphere",
"sphere*cylinder")
test_models(
"cylinder@hardsphere*sphere",
"sphere*cylinder@hardsphere")
test_models(
"barbell+sphere*cylinder@hardsphere",
"sphere*cylinder@hardsphere+barbell")
test_models(
"barbell+cylinder@hardsphere*sphere",
"cylinder@hardsphere*sphere+barbell")
test_models(
"barbell+sphere*cylinder@hardsphere",
"barbell+cylinder@hardsphere*sphere")
test_models(
"sphere*cylinder@hardsphere+barbell",
"cylinder@hardsphere*sphere+barbell")
test_models(
"barbell+sphere*cylinder@hardsphere",
"cylinder@hardsphere*sphere+barbell")
test_models(
"barbell+cylinder@hardsphere*sphere",
"sphere*cylinder@hardsphere+barbell")
[docs]def test_composite():
# type: () -> None
"""Check that model load works"""
from .product import RADIUS_ID, VOLFRAC_ID, STRUCTURE_MODE_ID, RADIUS_MODE_ID
#Test the the model produces the parameters that we would expect
model = load_model("cylinder@hardsphere*sphere")
actual = [p.name for p in model.info.parameters.kernel_parameters]
a_parts = ("sld", "sld_solvent", "radius", "length", "theta", "phi",
RADIUS_ID, VOLFRAC_ID, STRUCTURE_MODE_ID, RADIUS_MODE_ID)
b_parts = ("sld", "sld_solvent", "radius")
target = [*(f"A_{p}" for p in a_parts), *(f"B_{p}" for p in b_parts)]
assert target == actual, "%s != %s"%(target, actual)
[docs]def list_models_main():
# type: () -> int
"""
Run list_models as a main program. See :func:`list_models` for the
kinds of models that can be requested on the command line.
"""
import sys
kind = sys.argv[1] if len(sys.argv) > 1 else "all"
try:
models = list_models(kind)
print("\n".join(models))
except Exception:
print(list_models.__doc__)
return 1
return 0
if __name__ == "__main__":
list_models_main()