| 1 | """ |
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| 2 | Core model handling routines. |
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| 3 | """ |
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| 4 | from __future__ import print_function |
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| 5 | |
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| 6 | __all__ = [ |
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| 7 | "list_models", "load_model", "load_model_info", |
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| 8 | "build_model", "precompile_dlls", |
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| 9 | ] |
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| 10 | |
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| 11 | import os |
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| 12 | from os.path import basename, join as joinpath |
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| 13 | from glob import glob |
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| 14 | import re |
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| 15 | |
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| 16 | import numpy as np # type: ignore |
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| 17 | |
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| 18 | from . import generate |
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| 19 | from . import modelinfo |
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| 20 | from . import product |
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| 21 | from . import mixture |
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| 22 | from . import kernelpy |
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| 23 | from . import kernelcl |
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| 24 | from . import kerneldll |
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| 25 | from . import custom |
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| 26 | |
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| 27 | # pylint: disable=unused-import |
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| 28 | try: |
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| 29 | from typing import List, Union, Optional, Any |
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| 30 | from .kernel import KernelModel |
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| 31 | from .modelinfo import ModelInfo |
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| 32 | except ImportError: |
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| 33 | pass |
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| 34 | # pylint: enable=unused-import |
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| 35 | |
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| 36 | CUSTOM_MODEL_PATH = os.environ.get('SAS_MODELPATH', "") |
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| 37 | if CUSTOM_MODEL_PATH == "": |
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| 38 | CUSTOM_MODEL_PATH = joinpath(os.path.expanduser("~"), ".sasmodels", "custom_models") |
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| 39 | if not os.path.isdir(CUSTOM_MODEL_PATH): |
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| 40 | os.makedirs(CUSTOM_MODEL_PATH) |
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| 41 | |
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| 42 | # TODO: refactor composite model support |
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| 43 | # The current load_model_info/build_model does not reuse existing model |
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| 44 | # definitions when loading a composite model, instead reloading and |
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| 45 | # rebuilding the kernel for each component model in the expression. This |
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| 46 | # is fine in a scripting environment where the model is built when the script |
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| 47 | # starts and is thrown away when the script ends, but may not be the best |
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| 48 | # solution in a long-lived application. This affects the following functions: |
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| 49 | # |
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| 50 | # load_model |
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| 51 | # load_model_info |
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| 52 | # build_model |
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| 53 | |
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| 54 | KINDS = ("all", "py", "c", "double", "single", "opencl", "1d", "2d", |
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| 55 | "nonmagnetic", "magnetic") |
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| 56 | def list_models(kind=None): |
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| 57 | # type: (str) -> List[str] |
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| 58 | """ |
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| 59 | Return the list of available models on the model path. |
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| 60 | |
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| 61 | *kind* can be one of the following: |
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| 62 | |
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| 63 | * all: all models |
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| 64 | * py: python models only |
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| 65 | * c: compiled models only |
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| 66 | * single: models which support single precision |
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| 67 | * double: models which require double precision |
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| 68 | * opencl: controls if OpenCL is supperessed |
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| 69 | * 1d: models which are 1D only, or 2D using abs(q) |
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| 70 | * 2d: models which can be 2D |
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| 71 | * magnetic: models with an sld |
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| 72 | * nommagnetic: models without an sld |
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| 73 | |
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| 74 | For multiple conditions, combine with plus. For example, *c+single+2d* |
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| 75 | would return all oriented models implemented in C which can be computed |
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| 76 | accurately with single precision arithmetic. |
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| 77 | """ |
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| 78 | if kind and any(k not in KINDS for k in kind.split('+')): |
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| 79 | raise ValueError("kind not in " + ", ".join(KINDS)) |
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| 80 | files = sorted(glob(joinpath(generate.MODEL_PATH, "[a-zA-Z]*.py"))) |
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| 81 | available_models = [basename(f)[:-3] for f in files] |
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| 82 | if kind and '+' in kind: |
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| 83 | all_kinds = kind.split('+') |
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| 84 | condition = lambda name: all(_matches(name, k) for k in all_kinds) |
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| 85 | else: |
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| 86 | condition = lambda name: _matches(name, kind) |
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| 87 | selected = [name for name in available_models if condition(name)] |
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| 88 | |
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| 89 | return selected |
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| 90 | |
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| 91 | def _matches(name, kind): |
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| 92 | if kind is None or kind == "all": |
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| 93 | return True |
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| 94 | info = load_model_info(name) |
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| 95 | pars = info.parameters.kernel_parameters |
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| 96 | if kind == "py" and callable(info.Iq): |
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| 97 | return True |
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| 98 | elif kind == "c" and not callable(info.Iq): |
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| 99 | return True |
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| 100 | elif kind == "double" and not info.single: |
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| 101 | return True |
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| 102 | elif kind == "single" and info.single: |
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| 103 | return True |
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| 104 | elif kind == "opencl" and info.opencl: |
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| 105 | return True |
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| 106 | elif kind == "2d" and any(p.type == 'orientation' for p in pars): |
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| 107 | return True |
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| 108 | elif kind == "1d" and all(p.type != 'orientation' for p in pars): |
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| 109 | return True |
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| 110 | elif kind == "magnetic" and any(p.type == 'sld' for p in pars): |
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| 111 | return True |
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| 112 | elif kind == "nonmagnetic" and any(p.type != 'sld' for p in pars): |
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| 113 | return True |
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| 114 | return False |
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| 115 | |
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| 116 | def load_model(model_name, dtype=None, platform='ocl'): |
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| 117 | # type: (str, str, str) -> KernelModel |
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| 118 | """ |
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| 119 | Load model info and build model. |
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| 120 | |
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| 121 | *model_name* is the name of the model, or perhaps a model expression |
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| 122 | such as sphere*hardsphere or sphere+cylinder. |
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| 123 | |
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| 124 | *dtype* and *platform* are given by :func:`build_model`. |
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| 125 | """ |
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| 126 | return build_model(load_model_info(model_name), |
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| 127 | dtype=dtype, platform=platform) |
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| 128 | |
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| 129 | def load_model_info(model_string): |
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| 130 | # type: (str) -> modelinfo.ModelInfo |
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| 131 | """ |
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| 132 | Load a model definition given the model name. |
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| 133 | |
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| 134 | *model_string* is the name of the model, or perhaps a model expression |
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| 135 | such as sphere*cylinder or sphere+cylinder. Use '@' for a structure |
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| 136 | factor product, e.g. sphere@hardsphere. Custom models can be specified by |
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| 137 | prefixing the model name with 'custom.', e.g. 'custom.MyModel+sphere'. |
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| 138 | |
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| 139 | This returns a handle to the module defining the model. This can be |
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| 140 | used with functions in generate to build the docs or extract model info. |
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| 141 | """ |
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| 142 | |
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| 143 | if "+" in model_string: |
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| 144 | parts = [load_model_info(part) |
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| 145 | for part in model_string.split("+")] |
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| 146 | return mixture.make_mixture_info(parts, operation='+') |
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| 147 | elif "*" in model_string: |
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| 148 | parts = [load_model_info(part) |
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| 149 | for part in model_string.split("*")] |
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| 150 | return mixture.make_mixture_info(parts, operation='*') |
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| 151 | elif "@" in model_string: |
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| 152 | p_info, q_info = [load_model_info(part) |
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| 153 | for part in model_string.split("@")] |
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| 154 | return product.make_product_info(p_info, q_info) |
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| 155 | # We are now dealing with a pure model |
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| 156 | elif "custom." in model_string: |
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| 157 | pattern = "custom.([A-Za-z0-9_-]+)" |
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| 158 | result = re.match(pattern, model_string) |
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| 159 | if result is None: |
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| 160 | raise ValueError("Model name in invalid format: " + model_string) |
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| 161 | model_name = result.group(1) |
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| 162 | # Use ModelName to find the path to the custom model file |
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| 163 | model_path = joinpath(CUSTOM_MODEL_PATH, model_name + ".py") |
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| 164 | if not os.path.isfile(model_path): |
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| 165 | raise ValueError("The model file {} doesn't exist".format(model_path)) |
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| 166 | kernel_module = custom.load_custom_kernel_module(model_path) |
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| 167 | return modelinfo.make_model_info(kernel_module) |
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| 168 | kernel_module = generate.load_kernel_module(model_string) |
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| 169 | return modelinfo.make_model_info(kernel_module) |
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| 170 | |
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| 171 | |
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| 172 | def build_model(model_info, dtype=None, platform="ocl"): |
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| 173 | # type: (modelinfo.ModelInfo, str, str) -> KernelModel |
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| 174 | """ |
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| 175 | Prepare the model for the default execution platform. |
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| 176 | |
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| 177 | This will return an OpenCL model, a DLL model or a python model depending |
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| 178 | on the model and the computing platform. |
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| 179 | |
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| 180 | *model_info* is the model definition structure returned from |
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| 181 | :func:`load_model_info`. |
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| 182 | |
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| 183 | *dtype* indicates whether the model should use single or double precision |
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| 184 | for the calculation. Choices are 'single', 'double', 'quad', 'half', |
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| 185 | or 'fast'. If *dtype* ends with '!', then force the use of the DLL rather |
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| 186 | than OpenCL for the calculation. |
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| 187 | |
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| 188 | *platform* should be "dll" to force the dll to be used for C models, |
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| 189 | otherwise it uses the default "ocl". |
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| 190 | """ |
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| 191 | composition = model_info.composition |
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| 192 | if composition is not None: |
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| 193 | composition_type, parts = composition |
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| 194 | models = [build_model(p, dtype=dtype, platform=platform) for p in parts] |
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| 195 | if composition_type == 'mixture': |
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| 196 | return mixture.MixtureModel(model_info, models) |
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| 197 | elif composition_type == 'product': |
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| 198 | P, S = models |
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| 199 | return product.ProductModel(model_info, P, S) |
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| 200 | else: |
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| 201 | raise ValueError('unknown mixture type %s'%composition_type) |
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| 202 | |
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| 203 | # If it is a python model, return it immediately |
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| 204 | if callable(model_info.Iq): |
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| 205 | return kernelpy.PyModel(model_info) |
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| 206 | |
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| 207 | numpy_dtype, fast, platform = parse_dtype(model_info, dtype, platform) |
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| 208 | source = generate.make_source(model_info) |
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| 209 | if platform == "dll": |
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| 210 | #print("building dll", numpy_dtype) |
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| 211 | return kerneldll.load_dll(source['dll'], model_info, numpy_dtype) |
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| 212 | else: |
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| 213 | #print("building ocl", numpy_dtype) |
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| 214 | return kernelcl.GpuModel(source, model_info, numpy_dtype, fast=fast) |
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| 215 | |
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| 216 | def precompile_dlls(path, dtype="double"): |
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| 217 | # type: (str, str) -> List[str] |
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| 218 | """ |
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| 219 | Precompile the dlls for all builtin models, returning a list of dll paths. |
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| 220 | |
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| 221 | *path* is the directory in which to save the dlls. It will be created if |
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| 222 | it does not already exist. |
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| 223 | |
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| 224 | This can be used when build the windows distribution of sasmodels |
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| 225 | which may be missing the OpenCL driver and the dll compiler. |
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| 226 | """ |
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| 227 | numpy_dtype = np.dtype(dtype) |
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| 228 | if not os.path.exists(path): |
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| 229 | os.makedirs(path) |
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| 230 | compiled_dlls = [] |
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| 231 | for model_name in list_models(): |
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| 232 | model_info = load_model_info(model_name) |
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| 233 | if not callable(model_info.Iq): |
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| 234 | source = generate.make_source(model_info)['dll'] |
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| 235 | old_path = kerneldll.DLL_PATH |
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| 236 | try: |
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| 237 | kerneldll.DLL_PATH = path |
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| 238 | dll = kerneldll.make_dll(source, model_info, dtype=numpy_dtype) |
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| 239 | finally: |
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| 240 | kerneldll.DLL_PATH = old_path |
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| 241 | compiled_dlls.append(dll) |
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| 242 | return compiled_dlls |
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| 243 | |
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| 244 | def parse_dtype(model_info, dtype=None, platform=None): |
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| 245 | # type: (ModelInfo, str, str) -> (np.dtype, bool, str) |
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| 246 | """ |
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| 247 | Interpret dtype string, returning np.dtype and fast flag. |
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| 248 | |
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| 249 | Possible types include 'half', 'single', 'double' and 'quad'. If the |
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| 250 | type is 'fast', then this is equivalent to dtype 'single' but using |
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| 251 | fast native functions rather than those with the precision level |
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| 252 | guaranteed by the OpenCL standard. 'default' will choose the appropriate |
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| 253 | default for the model and platform. |
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| 254 | |
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| 255 | Platform preference can be specfied ("ocl" vs "dll"), with the default |
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| 256 | being OpenCL if it is availabe. If the dtype name ends with '!' then |
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| 257 | platform is forced to be DLL rather than OpenCL. |
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| 258 | |
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| 259 | This routine ignores the preferences within the model definition. This |
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| 260 | is by design. It allows us to test models in single precision even when |
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| 261 | we have flagged them as requiring double precision so we can easily check |
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| 262 | the performance on different platforms without having to change the model |
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| 263 | definition. |
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| 264 | """ |
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| 265 | # Assign default platform, overriding ocl with dll if OpenCL is unavailable |
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| 266 | # If opencl=False OpenCL is switched off |
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| 267 | if platform is None: |
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| 268 | platform = "ocl" |
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| 269 | if not kernelcl.use_opencl() or not model_info.opencl: |
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| 270 | platform = "dll" |
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| 271 | |
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| 272 | # Check if type indicates dll regardless of which platform is given |
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| 273 | if dtype is not None and dtype.endswith('!'): |
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| 274 | platform = "dll" |
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| 275 | dtype = dtype[:-1] |
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| 276 | |
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| 277 | # Convert special type names "half", "fast", and "quad" |
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| 278 | fast = (dtype == "fast") |
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| 279 | if fast: |
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| 280 | dtype = "single" |
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| 281 | elif dtype == "quad": |
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| 282 | dtype = "longdouble" |
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| 283 | elif dtype == "half": |
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| 284 | dtype = "float16" |
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| 285 | |
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| 286 | # Convert dtype string to numpy dtype. |
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| 287 | if dtype is None or dtype == "default": |
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| 288 | numpy_dtype = (generate.F32 if platform == "ocl" and model_info.single |
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| 289 | else generate.F64) |
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| 290 | else: |
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| 291 | numpy_dtype = np.dtype(dtype) |
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| 292 | # Make sure that the type is supported by opencl, otherwise use dll |
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| 293 | if platform == "ocl": |
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| 294 | env = kernelcl.environment() |
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| 295 | if not env.has_type(numpy_dtype): |
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| 296 | platform = "dll" |
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| 297 | if dtype is None: |
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| 298 | numpy_dtype = generate.F64 |
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| 299 | |
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| 300 | return numpy_dtype, fast, platform |
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| 301 | |
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| 302 | def list_models_main(): |
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| 303 | # type: () -> None |
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| 304 | """ |
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| 305 | Run list_models as a main program. See :func:`list_models` for the |
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| 306 | kinds of models that can be requested on the command line. |
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| 307 | """ |
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| 308 | import sys |
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| 309 | kind = sys.argv[1] if len(sys.argv) > 1 else "all" |
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| 310 | print("\n".join(list_models(kind))) |
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| 311 | |
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| 312 | def test_composite_order(): |
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| 313 | def test_models(fst, snd): |
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| 314 | """Confirm that two models produce the same parameters""" |
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| 315 | fst = load_model(fst) |
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| 316 | snd = load_model(snd) |
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| 317 | # Un-disambiguate parameter names so that we can check if the same |
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| 318 | # parameters are in a pair of composite models. Since each parameter in |
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| 319 | # the mixture model is tagged as e.g., A_sld, we ought to use a |
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| 320 | # regex subsitution s/^[A-Z]+_/_/, but removing all uppercase letters |
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| 321 | # is good enough. |
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| 322 | fst = [[x for x in p.name if x == x.lower()] for p in fst.info.parameters.kernel_parameters] |
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| 323 | snd = [[x for x in p.name if x == x.lower()] for p in snd.info.parameters.kernel_parameters] |
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| 324 | assert sorted(fst) == sorted(snd), "{} != {}".format(fst, snd) |
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| 325 | |
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| 326 | def build_test(first, second): |
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| 327 | test = lambda description: test_models(first, second) |
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| 328 | description = first + " vs. " + second |
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| 329 | return test, description |
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| 330 | |
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| 331 | yield build_test( |
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| 332 | "cylinder+sphere", |
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| 333 | "sphere+cylinder") |
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| 334 | yield build_test( |
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| 335 | "cylinder*sphere", |
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| 336 | "sphere*cylinder") |
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| 337 | yield build_test( |
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| 338 | "cylinder@hardsphere*sphere", |
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| 339 | "sphere*cylinder@hardsphere") |
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| 340 | yield build_test( |
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| 341 | "barbell+sphere*cylinder@hardsphere", |
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| 342 | "sphere*cylinder@hardsphere+barbell") |
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| 343 | yield build_test( |
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| 344 | "barbell+cylinder@hardsphere*sphere", |
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| 345 | "cylinder@hardsphere*sphere+barbell") |
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| 346 | yield build_test( |
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| 347 | "barbell+sphere*cylinder@hardsphere", |
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| 348 | "barbell+cylinder@hardsphere*sphere") |
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| 349 | yield build_test( |
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| 350 | "sphere*cylinder@hardsphere+barbell", |
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| 351 | "cylinder@hardsphere*sphere+barbell") |
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| 352 | yield build_test( |
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| 353 | "barbell+sphere*cylinder@hardsphere", |
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| 354 | "cylinder@hardsphere*sphere+barbell") |
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| 355 | yield build_test( |
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| 356 | "barbell+cylinder@hardsphere*sphere", |
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| 357 | "sphere*cylinder@hardsphere+barbell") |
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| 358 | |
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| 359 | def test_composite(): |
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| 360 | # type: () -> None |
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| 361 | """Check that model load works""" |
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| 362 | #Test the the model produces the parameters that we would expect |
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| 363 | model = load_model("cylinder@hardsphere*sphere") |
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| 364 | actual = [p.name for p in model.info.parameters.kernel_parameters] |
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| 365 | target = ("sld sld_solvent radius length theta phi volfraction" |
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| 366 | " A_sld A_sld_solvent A_radius").split() |
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| 367 | assert target == actual, "%s != %s"%(target, actual) |
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| 368 | |
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| 369 | if __name__ == "__main__": |
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| 370 | list_models_main() |
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