field.py 25 KB
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from enum import Enum
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from itertools import chain
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from typing import Tuple, Sequence, Optional, List
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import numpy as np
import sympy as sp
from sympy.core.cache import cacheit
from sympy.tensor import IndexedBase
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from pystencils.assignment import Assignment
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from pystencils.alignedarray import aligned_empty
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from pystencils.data_types import TypedSymbol, create_type, create_composite_type_from_string, StructType
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from pystencils.sympyextensions import is_integer_sequence
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class FieldType(Enum):
    # generic fields
    GENERIC = 0
    # index fields are currently only used for boundary handling
    # the coordinates are not the loop counters in that case, but are read from this index field
    INDEXED = 1
    # communication buffer, used for (un)packing data in communication.
    BUFFER = 2

    @staticmethod
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    def is_generic(field):
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        assert isinstance(field, Field)
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        return field.field_type == FieldType.GENERIC
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    @staticmethod
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    def is_indexed(field):
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        assert isinstance(field, Field)
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        return field.field_type == FieldType.INDEXED
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    @staticmethod
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    def is_buffer(field):
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        assert isinstance(field, Field)
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        return field.field_type == FieldType.BUFFER
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class Field(object):
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    """
    With fields one can formulate stencil-like update rules on structured grids.
    This Field class knows about the dimension, memory layout (strides) and optionally about the size of an array.

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    Creating Fields:

        To create a field use one of the static create* members. There are two options:

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        1. create a kernel with fixed loop sizes i.e. the shape of the array is already known. This is usually the
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           case if just-in-time compilation directly from Python is done. (see :func:`Field.create_from_numpy_array`)
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        2. create a more general kernel that works for variable array sizes. This can be used to create kernels
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           beforehand for a library. (see :func:`Field.create_generic`)
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    Dimensions:
        A field has spatial and index dimensions, where the spatial dimensions come first.
        The interpretation is that the field has multiple cells in (usually) two or three dimensional space which are
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        looped over. Additionally  N values are stored per cell. In this case spatial_dimensions is two or three,
        and index_dimensions equals N. If you want to store a matrix on each point in a two dimensional grid, there
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        are four dimensions, two spatial and two index dimensions: ``len(arr.shape) == spatial_dims + index_dims``
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    Indexing:
        When accessing (indexing) a field the result is a FieldAccess which is derived from sympy Symbol.
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        First specify the spatial offsets in [], then in case index_dimension>0 the indices in ()
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        e.g. ``f[-1,0,0](7)``
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    Example without index dimensions:
        >>> a = np.zeros([10, 10])
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        >>> f = Field.create_from_numpy_array("f", a, index_dimensions=0)
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        >>> jacobi = ( f[-1,0] + f[1,0] + f[0,-1] + f[0,1] ) / 4

    Example with index dimensions: LBM D2Q9 stream pull
        >>> stencil = np.array([[0,0], [0,1], [0,-1]])
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        >>> src = Field.create_generic("src", spatial_dimensions=2, index_dimensions=1)
        >>> dst = Field.create_generic("dst", spatial_dimensions=2, index_dimensions=1)
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        >>> for i, offset in enumerate(stencil):
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        ...     Assignment(dst[0,0](i), src[-offset](i))
        Assignment(dst_C^0, src_C^0)
        Assignment(dst_C^1, src_S^1)
        Assignment(dst_C^2, src_N^2)
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    """
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    @staticmethod
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    def create_generic(field_name, spatial_dimensions, dtype=np.float64, index_dimensions=0, layout='numpy',
                       index_shape=None, field_type=FieldType.GENERIC) -> 'Field':
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        """
        Creates a generic field where the field size is not fixed i.e. can be called with arrays of different sizes

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        Args:
            field_name: symbolic name for the field
            dtype: numpy data type of the array the kernel is called with later
            spatial_dimensions: see documentation of Field
            index_dimensions: see documentation of Field
            layout: tuple specifying the loop ordering of the spatial dimensions e.g. (2, 1, 0 ) means that
                    the outer loop loops over dimension 2, the second outer over dimension 1, and the inner loop
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                    over dimension 0. Also allowed: the strings 'numpy' (0,1,..d) or 'reverse_numpy' (d, ..., 1, 0)
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            index_shape: optional shape of the index dimensions i.e. maximum values allowed for each index dimension,
                        has to be a list or tuple
            field_type: besides the normal GENERIC fields, there are INDEXED fields that store indices of the domain
                        that should be iterated over, and BUFFER fields that are used to generate
                        communication packing/unpacking kernels
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        """
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        if isinstance(layout, str):
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            layout = spatial_layout_string_to_tuple(layout, dim=spatial_dimensions)
        shape_symbol = IndexedBase(TypedSymbol(Field.SHAPE_PREFIX + field_name, Field.SHAPE_DTYPE), shape=(1,))
        stride_symbol = IndexedBase(TypedSymbol(Field.STRIDE_PREFIX + field_name, Field.STRIDE_DTYPE), shape=(1,))
        total_dimensions = spatial_dimensions + index_dimensions
        if index_shape is None or len(index_shape) == 0:
            shape = tuple([shape_symbol[i] for i in range(total_dimensions)])
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        else:
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            shape = tuple([shape_symbol[i] for i in range(spatial_dimensions)] + list(index_shape))
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        strides = tuple([stride_symbol[i] for i in range(total_dimensions)])
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        np_data_type = np.dtype(dtype)
        if np_data_type.fields is not None:
            if index_dimensions != 0:
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                raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
            shape += (1,)
            strides += (1,)

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        return Field(field_name, field_type, dtype, layout, shape, strides)
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    @staticmethod
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    def create_from_numpy_array(field_name: str, array: np.ndarray, index_dimensions: int = 0) -> 'Field':
        """Creates a field based on the layout, data type, and shape of a given numpy array.

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        Kernels created for these kind of fields can only be called with arrays of the same layout, shape and type.
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        Args:
            field_name: symbolic name for the field
            array: numpy array
            index_dimensions: see documentation of Field
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        """
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        spatial_dimensions = len(array.shape) - index_dimensions
        if spatial_dimensions < 1:
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            raise ValueError("Too many index dimensions. At least one spatial dimension required")

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        full_layout = get_layout_of_array(array)
        spatial_layout = tuple([i for i in full_layout if i < spatial_dimensions])
        assert len(spatial_layout) == spatial_dimensions
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        strides = tuple([s // np.dtype(array.dtype).itemsize for s in array.strides])
        shape = tuple(int(s) for s in array.shape)
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        numpy_dtype = np.dtype(array.dtype)
        if numpy_dtype.fields is not None:
            if index_dimensions != 0:
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                raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
            shape += (1,)
            strides += (1,)

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        return Field(field_name, FieldType.GENERIC, array.dtype, spatial_layout, shape, strides)
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    @staticmethod
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    def create_fixed_size(field_name: str, shape: Tuple[int, ...], index_dimensions: int = 0,
                          dtype=np.float64, layout: str = 'numpy', strides: Optional[Sequence[int]]=None) -> 'Field':
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        """
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        Creates a field with fixed sizes i.e. can be called only with arrays of the same size and layout
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        Args:
            field_name: symbolic name for the field
            shape: overall shape of the array
            index_dimensions: how many of the trailing dimensions are interpreted as index (as opposed to spatial)
            dtype: numpy data type of the array the kernel is called with later
            layout: full layout of array, not only spatial dimensions
            strides: strides in bytes or None to automatically compute them from shape (assuming no padding)
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        """
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        spatial_dimensions = len(shape) - index_dimensions
        assert spatial_dimensions >= 1
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        if isinstance(layout, str):
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            layout = layout_string_to_tuple(layout, spatial_dimensions + index_dimensions)
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        shape = tuple(int(s) for s in shape)
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        if strides is None:
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            strides = compute_strides(shape, layout)
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        else:
            assert len(strides) == len(shape)
            strides = tuple([s // np.dtype(dtype).itemsize for s in strides])
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        numpy_dtype = np.dtype(dtype)
        if numpy_dtype.fields is not None:
            if index_dimensions != 0:
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                raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
            shape += (1,)
            strides += (1,)

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        spatial_layout = list(layout)
        for i in range(spatial_dimensions, len(layout)):
            spatial_layout.remove(i)
        return Field(field_name, FieldType.GENERIC, dtype, tuple(spatial_layout), shape, strides)
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    def __init__(self, field_name, field_type, dtype, layout, shape, strides):
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        """Do not use directly. Use static create* methods"""
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        self._fieldName = field_name
        assert isinstance(field_type, FieldType)
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        self.field_type = field_type
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        self._dtype = create_type(dtype)
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        self._layout = normalize_layout(layout)
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        self.shape = shape
        self.strides = strides
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        self.latex_name: Optional[str] = None
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    def new_field_with_different_name(self, new_name):
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        return Field(new_name, self.field_type, self._dtype, self._layout, self.shape, self.strides)
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    @property
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    def spatial_dimensions(self) -> int:
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        return len(self._layout)

    @property
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    def index_dimensions(self) -> int:
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        return len(self.shape) - len(self._layout)
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    @property
    def layout(self):
        return self._layout

    @property
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    def name(self) -> str:
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        return self._fieldName

    @property
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    def spatial_shape(self) -> Tuple[int, ...]:
        return self.shape[:self.spatial_dimensions]
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    @property
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    def has_fixed_shape(self):
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        return is_integer_sequence(self.shape)
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    @property
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    def index_shape(self):
        return self.shape[self.spatial_dimensions:]
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    @property
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    def has_fixed_index_shape(self):
        return is_integer_sequence(self.index_shape)
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    @property
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    def spatial_strides(self):
        return self.strides[:self.spatial_dimensions]
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    @property
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    def index_strides(self):
        return self.strides[self.spatial_dimensions:]
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    @property
    def dtype(self):
        return self._dtype

    def __repr__(self):
        return self._fieldName

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    def neighbor(self, coord_id, offset):
        offset_list = [0] * self.spatial_dimensions
        offset_list[coord_id] = offset
        return Field.Access(self, tuple(offset_list))
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    def neighbors(self, stencil):
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        return [self.__getitem__(s) for s in stencil]
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    @property
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    def center_vector(self):
        index_shape = self.index_shape
        if len(index_shape) == 0:
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            return self.center
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        elif len(index_shape) == 1:
            return sp.Matrix([self(i) for i in range(index_shape[0])])
        elif len(index_shape) == 2:
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            def cb(*args):
                r = self.__call__(*args)
                return r
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            return sp.Matrix(*index_shape, cb)
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    @property
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    def center(self):
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        center = tuple([0] * self.spatial_dimensions)
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        return Field.Access(self, center)

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    def __getitem__(self, offset):
        if type(offset) is np.ndarray:
            offset = tuple(offset)
        if type(offset) is str:
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            offset = tuple(direction_string_to_offset(offset, self.spatial_dimensions))
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        if type(offset) is not tuple:
            offset = (offset,)
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        if len(offset) != self.spatial_dimensions:
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            raise ValueError("Wrong number of spatial indices: "
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                             "Got %d, expected %d" % (len(offset), self.spatial_dimensions))
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        return Field.Access(self, offset)

    def __call__(self, *args, **kwargs):
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        center = tuple([0] * self.spatial_dimensions)
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        return Field.Access(self, center)(*args, **kwargs)

    def __hash__(self):
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        return hash((self._layout, self.shape, self.strides, self._dtype, self.field_type, self._fieldName))
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    def __eq__(self, other):
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        self_tuple = (self.shape, self.strides, self.name, self.dtype, self.field_type)
        other_tuple = (other.shape, other.strides, other.name, other.dtype, other.field_type)
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        return self_tuple == other_tuple
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    PREFIX = "f"
    STRIDE_PREFIX = PREFIX + "stride_"
    SHAPE_PREFIX = PREFIX + "shape_"
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    STRIDE_DTYPE = create_composite_type_from_string("const int *")
    SHAPE_DTYPE = create_composite_type_from_string("const int *")
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    DATA_PREFIX = PREFIX + "d_"
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    # noinspection PyAttributeOutsideInit,PyUnresolvedReferences
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    class Access(sp.Symbol):
        def __new__(cls, name, *args, **kwargs):
            obj = Field.Access.__xnew_cached_(cls, name, *args, **kwargs)
            return obj

        def __new_stage2__(self, field, offsets=(0, 0, 0), idx=None):
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            field_name = field.name
            offsets_and_index = chain(offsets, idx) if idx is not None else offsets
            constant_offsets = not any([isinstance(o, sp.Basic) and not o.is_Integer for o in offsets_and_index])
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            if not idx:
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                idx = tuple([0] * field.index_dimensions)
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            if constant_offsets:
                offset_name = offset_to_direction_string(offsets)
                if field.index_dimensions == 0:
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                    superscript = None
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                elif field.index_dimensions == 1:
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                    superscript = str(idx[0])
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                else:
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                    idx_str = ",".join([str(e) for e in idx])
                    superscript = idx_str
                if field.has_fixed_index_shape and not isinstance(field.dtype, StructType):
                    for i, bound in zip(idx, field.index_shape):
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                        if i >= bound:
                            raise ValueError("Field index out of bounds")
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            else:
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                offset_name = "%0.10X" % (abs(hash(tuple(offsets_and_index))))
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                superscript = None
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            symbol_name = "%s_%s" % (field_name, offset_name)
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            if superscript is not None:
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                symbol_name += "^" + superscript
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            obj = super(Field.Access, self).__xnew__(self, symbol_name)
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            obj._field = field
            obj._offsets = []
            for o in offsets:
                if isinstance(o, sp.Basic):
                    obj._offsets.append(o)
                else:
                    obj._offsets.append(int(o))
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            obj._offsetName = offset_name
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            obj._superscript = superscript
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            obj._index = idx

            return obj

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        def __getnewargs__(self):
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            return self.field, self.offsets, self.index
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        # noinspection SpellCheckingInspection
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        __xnew__ = staticmethod(__new_stage2__)
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        # noinspection SpellCheckingInspection
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        __xnew_cached_ = staticmethod(cacheit(__new_stage2__))

        def __call__(self, *idx):
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            if self._index != tuple([0]*self.field.index_dimensions):
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                raise ValueError("Indexing an already indexed Field.Access")

            idx = tuple(idx)
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            if self.field.index_dimensions == 0 and idx == (0,):
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                idx = ()

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            if len(idx) != self.field.index_dimensions:
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                raise ValueError("Wrong number of indices: "
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                                 "Got %d, expected %d" % (len(idx), self.field.index_dimensions))
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            return Field.Access(self.field, self._offsets, idx)

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        def __getitem__(self, *idx):
            return self.__call__(*idx)

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        def __iter__(self):
            """This is necessary to work with parts of sympy that test if an object is iterable (e.g. simplify).
            The __getitem__ would make it iterable"""
            raise TypeError("Field access is not iterable")

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        @property
        def field(self):
            return self._field

        @property
        def offsets(self):
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            return tuple(self._offsets)
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        @property
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        def required_ghost_layers(self):
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            return int(np.max(np.abs(self._offsets)))

        @property
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        def nr_of_coordinates(self):
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            return len(self._offsets)

        @property
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        def offset_name(self) -> str:
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            return self._offsetName

        @property
        def index(self):
            return self._index

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        def neighbor(self, coord_id: int, offset: Sequence[int]) -> 'Field.Access':
            offset_list = list(self.offsets)
            offset_list[coord_id] += offset
            return Field.Access(self.field, tuple(offset_list), self.index)
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        def get_shifted(self, *shift)-> 'Field.Access':
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            return Field.Access(self.field, tuple(a + b for a, b in zip(shift, self.offsets)), self.index)

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        def _hashable_content(self):
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            super_class_contents = list(super(Field.Access, self)._hashable_content())
            t = tuple(super_class_contents + [hash(self._field), self._index] + self._offsets)
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            return t
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        def _latex(self, _):
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            n = self._field.latex_name if self._field.latex_name else self._field.name
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            if self._superscript:
                return "{{%s}_{%s}^{%s}}" % (n, self._offsetName, self._superscript)
            else:
                return "{{%s}_{%s}}" % (n, self._offsetName)
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def get_layout_from_strides(strides: Sequence[int], index_dimension_ids: Optional[List[int]] = None):
    index_dimension_ids = [] if index_dimension_ids is None else index_dimension_ids
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    coordinates = list(range(len(strides)))
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    relevant_strides = [stride for i, stride in enumerate(strides) if i not in index_dimension_ids]
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    result = [x for (y, x) in sorted(zip(relevant_strides, coordinates), key=lambda pair: pair[0], reverse=True)]
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    return normalize_layout(result)
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def get_layout_of_array(arr: np.ndarray, index_dimension_ids: Optional[List[int]] = None):
    """ Returns a list indicating the memory layout (linearization order) of the numpy array.

    Examples:
        >>> get_layout_of_array(np.zeros([3,3,3]))
        (0, 1, 2)
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    In this example the loop over the zeroth coordinate should be the outermost loop,
    followed by the first and second. Elements arr[x,y,0] and arr[x,y,1] are adjacent in memory.
    Normally constructed numpy arrays have this order, however by stride tricks or other frameworks, arrays
    with different memory layout can be created.
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    The index_dimension_ids parameter leaves specifies which coordinates should not be
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    """
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    index_dimension_ids = [] if index_dimension_ids is None else index_dimension_ids
    return get_layout_from_strides(arr.strides, index_dimension_ids)
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def create_numpy_array_with_layout(shape, layout, alignment=False, byte_offset=0, **kwargs):
    """Creates numpy array with given memory layout.

    Args:
        shape: shape of the resulting array
        layout: layout as tuple, where the coordinates are ordered from slow to fast
        alignment: number of bytes to align the beginning and the innermost coordinate to, or False for no alignment
        byte_offset: only used when alignment is specified, align not beginning but address at this offset
                     mostly used to align first inner cell, not ghost cells

    Example:
        >>> res = create_numpy_array_with_layout(shape=(2, 3, 4, 5), layout=(3, 2, 0, 1))
        >>> res.shape
        (2, 3, 4, 5)
        >>> get_layout_of_array(res)
        (3, 2, 0, 1)
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    """
    assert set(layout) == set(range(len(shape))), "Wrong layout descriptor"
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    cur_layout = list(range(len(shape)))
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    swaps = []
    for i in range(len(layout)):
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        if cur_layout[i] != layout[i]:
            index_to_swap_with = cur_layout.index(layout[i])
            swaps.append((i, index_to_swap_with))
            cur_layout[i], cur_layout[index_to_swap_with] = cur_layout[index_to_swap_with], cur_layout[i]
    assert tuple(cur_layout) == tuple(layout)
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    shape = list(shape)
    for a, b in swaps:
        shape[a], shape[b] = shape[b], shape[a]

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    if not alignment:
        res = np.empty(shape, order='c', **kwargs)
    else:
        if alignment is True:
            alignment = 8 * 4
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        res = aligned_empty(shape, alignment, byte_offset=byte_offset, **kwargs)
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    for a, b in reversed(swaps):
        res = res.swapaxes(a, b)
    return res


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def spatial_layout_string_to_tuple(layout_str: str, dim: int) -> Tuple[int, ...]:
    if layout_str in ('fzyx', 'zyxf'):
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        assert dim <= 3
        return tuple(reversed(range(dim)))
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    if layout_str in ('fzyx', 'f', 'reverse_numpy', 'SoA'):
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        return tuple(reversed(range(dim)))
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    elif layout_str in ('c', 'numpy', 'AoS'):
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        return tuple(range(dim))
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    raise ValueError("Unknown layout descriptor " + layout_str)
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def layout_string_to_tuple(layout_str, dim):
    layout_str = layout_str.lower()
    if layout_str == 'fzyx' or layout_str == 'soa':
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        assert dim <= 4
        return tuple(reversed(range(dim)))
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    elif layout_str == 'zyxf' or layout_str == 'aos':
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        assert dim <= 4
        return tuple(reversed(range(dim - 1))) + (dim-1,)
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    elif layout_str == 'f' or layout_str == 'reverse_numpy':
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        return tuple(reversed(range(dim)))
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    elif layout_str == 'c' or layout_str == 'numpy':
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        return tuple(range(dim))
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    raise ValueError("Unknown layout descriptor " + layout_str)
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def normalize_layout(layout):
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    """Takes a layout tuple and subtracts the minimum from all entries"""
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    min_entry = min(layout)
    return tuple(i - min_entry for i in layout)
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def compute_strides(shape, layout):
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    """
    Computes strides assuming no padding exists
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    Args:
        shape: shape (size) of array
        layout: layout specification as tuple

    Returns:
        strides in elements, not in bytes
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    """
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    dim = len(shape)
    assert len(layout) == dim
    assert len(set(layout)) == dim
    strides = [0] * dim
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    product = 1
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    for j in reversed(layout):
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        strides[j] = product
        product *= shape[j]
    return tuple(strides)
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def offset_component_to_direction_string(coordinate_id: int, value: int) -> str:
    """Translates numerical offset to string notation.

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    x offsets are labeled with east 'E' and 'W',
    y offsets with north 'N' and 'S' and
    z offsets with top 'T' and bottom 'B'
    If the absolute value of the offset is bigger than 1, this number is prefixed.

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    Args:
        coordinate_id: integer 0, 1 or 2 standing for x,y and z
        value: integer offset

    Examples:
        >>> offset_component_to_direction_string(0, 1)
        'E'
        >>> offset_component_to_direction_string(1, 2)
        '2N'
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    """
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    name_components = (('W', 'E'),  # west, east
                       ('S', 'N'),  # south, north
                       ('B', 'T'),  # bottom, top
                       )
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    if value == 0:
        result = ""
    elif value < 0:
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        result = name_components[coordinate_id][0]
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    else:
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        result = name_components[coordinate_id][1]
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    if abs(value) > 1:
        result = "%d%s" % (abs(value), result)
    return result


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def offset_to_direction_string(offsets: Sequence[int]) -> str:
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    """
    Translates numerical offset to string notation.
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    For details see :func:`offset_component_to_direction_string`
    Args:
        offsets: 3-tuple with x,y,z offset

    Examples:
        >>> offset_to_direction_string([1, -1, 0])
        'SE'
        >>> offset_to_direction_string(([-3, 0, -2]))
        '2B3W'
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    """
    names = ["", "", ""]
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    for i in range(len(offsets)):
        names[i] = offset_component_to_direction_string(i, offsets[i])
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    name = "".join(reversed(names))
    if name == "":
        name = "C"
    return name


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def direction_string_to_offset(direction: str, dim: int = 3):
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    """
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    Reverse mapping of :func:`offset_to_direction_string`
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    Args:
        direction: string representation of offset
        dim: dimension of offset, i.e the length of the returned list

    Examples:
        >>> direction_string_to_offset('NW', dim=3)
        array([-1,  1,  0])
        >>> direction_string_to_offset('NW', dim=2)
        array([-1,  1])
        >>> direction_string_to_offset(offset_to_direction_string((3,-2,1)))
        array([ 3, -2,  1])
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    """
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    offset_dict = {
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        'C': np.array([0, 0, 0]),

        'W': np.array([-1, 0, 0]),
        'E': np.array([1, 0, 0]),

        'S': np.array([0, -1, 0]),
        'N': np.array([0, 1, 0]),

        'B': np.array([0, 0, -1]),
        'T': np.array([0, 0, 1]),
    }
    offset = np.array([0, 0, 0])

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    while len(direction) > 0:
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        factor = 1
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        first_non_digit = 0
        while direction[first_non_digit].isdigit():
            first_non_digit += 1
        if first_non_digit > 0:
            factor = int(direction[:first_non_digit])
            direction = direction[first_non_digit:]
        cur_offset = offset_dict[direction[0]]
        offset += factor * cur_offset
        direction = direction[1:]
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    return offset[:dim]