Lightstar/venv/Lib/site-packages/functorch/dim/reference.py

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# reference python implementations for C ops
import torch

from functorch._C import dim as _C
from . import op_properties
from .batch_tensor import _enable_layers
from .tree_map import tree_flatten, tree_map

DimList = _C.DimList
import operator
from functools import reduce


# use dict to avoid writing C++ bindings for set
pointwise = set(op_properties.pointwise)


def prod(x):
    return reduce(operator.mul, x, 1)


def _wrap_dim(d, N, keepdim):
    from . import Dim

    if isinstance(d, Dim):
        assert not keepdim, "cannot preserve first-class dimensions with keepdim=True"
        return d
    elif d >= 0:
        return d - N
    else:
        return d


def _dims(d, N, keepdim, single_dim):
    from . import Dim

    if isinstance(d, (Dim, int)):
        return ltuple((_wrap_dim(d, N, keepdim),))
    assert not single_dim, f"expected a single dimension or int but found: {d}"
    return ltuple(_wrap_dim(x, N, keepdim) for x in d)


def _bind_dims_to_size(lhs_size, rhs, lhs_debug):
    from . import DimensionMismatchError

    not_bound = tuple((i, r) for i, r in enumerate(rhs) if not r.is_bound)
    if len(not_bound) == 1:
        idx, d = not_bound[0]
        rhs_so_far = prod(r.size for r in rhs if r.is_bound)
        if lhs_size % rhs_so_far != 0:
            rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs)
            raise DimensionMismatchError(
                f"inferred dimension does not evenly fit into larger dimension: {lhs_size} vs {rhs_s}"
            )
        new_size = lhs_size // rhs_so_far
        d.size = new_size
    elif len(not_bound) > 1:
        rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs)
        raise DimensionMismatchError(
            f"cannot infer the size of two dimensions at once: {rhs} with sizes {rhs_s}"
        )
    else:
        rhs_size = prod(r.size for r in rhs)
        if lhs_size != rhs_size:
            raise DimensionMismatchError(
                f"Dimension sizes to do not match ({lhs_size} != {rhs_size}) when matching {lhs_debug} to {rhs}"
            )


def _tensor_levels(inp):
    from . import _Tensor

    if isinstance(inp, _Tensor):
        return inp._tensor, llist(inp._levels), inp._has_device
    else:
        return inp, llist(range(-inp.ndim, 0)), True


def _match_levels(v, from_levels, to_levels):
    view = []
    permute = []
    requires_view = False
    size = v.size()
    for t in to_levels:
        try:
            idx = from_levels.index(t)
            permute.append(idx)
            view.append(size[idx])
        except ValueError:
            view.append(1)
            requires_view = True
    if permute != list(range(len(permute))):
        v = v.permute(*permute)
    if requires_view:
        v = v.view(*view)
    return v


# make a single dimension positional but do not permute it,
# used to do multi-tensor operators where the dim being acted on
# should not physically move if possible
def _positional_no_permute(self, dim, expand_dim=False):
    from . import Tensor

    ptensor, levels = self._tensor, llist(self._levels)
    try:
        idx = levels.index(dim)
    except ValueError:
        if not expand_dim:
            raise
        idx = 0
        ptensor = ptensor.expand(dim.size, *ptensor.size())
        levels.insert(0, 0)
    idx_batched = 0
    for i in range(idx):
        if isinstance(levels[i], int):
            levels[i] -= 1
            idx_batched += 1
    levels[idx] = -idx_batched - 1
    return Tensor.from_positional(ptensor, levels, self._has_device), idx_batched


def seq(a, b):
    from . import Dim

    if isinstance(a, Dim) != isinstance(b, Dim):
        return False
    if isinstance(a, Dim):
        return a is b
    else:
        return a == b


class isin:
    def __contains__(self, item):
        for x in self:
            if seq(item, x):
                return True
        return False

    def index(self, item):
        for i, x in enumerate(self):
            if seq(item, x):
                return i
        raise ValueError


class llist(isin, list):
    pass


class ltuple(isin, tuple):
    pass


empty_dict = {}


@classmethod
def __torch_function__(self, orig, cls, args, kwargs=empty_dict):
    from . import _Tensor, Tensor, TensorLike
    from .delayed_mul_tensor import DelayedMulTensor

    if orig is torch.Tensor.__mul__:
        lhs, rhs = args
        if (
            isinstance(lhs, _Tensor)
            and isinstance(rhs, _Tensor)
            and lhs.ndim == 0
            and rhs.ndim == 0
        ):
            return DelayedMulTensor(lhs, rhs)
    all_dims = llist()
    flat_args, unflatten = tree_flatten((args, kwargs))
    device_holding_tensor = None
    for f in flat_args:
        if isinstance(f, _Tensor):
            if f._has_device:
                device_holding_tensor = f._batchtensor
            for d in f.dims:
                if d not in all_dims:
                    all_dims.append(d)

    def unwrap(t):
        if isinstance(t, _Tensor):
            r = t._batchtensor
            if device_holding_tensor is not None and not t._has_device:
                r = r.to(device=device_holding_tensor.device)
            return r
        return t

    if orig in pointwise:
        result_levels = llist()
        arg_levels = llist()
        to_expand = []
        for i, f in enumerate(flat_args):
            if isinstance(f, TensorLike):
                ptensor, levels, _ = _tensor_levels(f)
                if (
                    isinstance(f, _Tensor)
                    and not f._has_device
                    and device_holding_tensor is not None
                ):
                    ptensor = ptensor.to(device=device_holding_tensor.device)
                flat_args[i] = ptensor
                for l in levels:
                    if l not in result_levels:
                        result_levels.append(l)
                to_expand.append((i, levels))

        for i, levels in to_expand:
            flat_args[i] = _match_levels(flat_args[i], levels, result_levels)
        args, kwargs = unflatten(flat_args)
        result = orig(*args, **kwargs)

        def wrap(t):
            if isinstance(t, TensorLike):
                return Tensor.from_positional(
                    t, result_levels, device_holding_tensor is not None
                )
            return t

        return tree_map(wrap, result)
    else:

        def wrap(t):
            if isinstance(t, TensorLike):
                return Tensor.from_batched(t, device_holding_tensor is not None)
            return t

        with _enable_layers(all_dims):
            print(f"batch_tensor for {orig}")
            args, kwargs = unflatten(unwrap(f) for f in flat_args)
            result = orig(*args, **kwargs)
            # print("END", orig)
            return tree_map(wrap, result)


def positional(self, *dims):
    from . import Dim, DimensionBindError, Tensor

    ptensor, levels = self._tensor, llist(self._levels)
    flat_dims = llist()
    view = []
    needs_view = False
    ndim = self.ndim
    for d in dims:
        if isinstance(d, DimList):
            flat_dims.extend(d)
            view.extend(e.size for e in d)
        elif isinstance(d, Dim):
            flat_dims.append(d)
            view.append(d.size)
        elif isinstance(d, int):
            d = _wrap_dim(d, ndim, False)
            flat_dims.append(d)
            view.append(ptensor.size(d))
        else:
            flat_dims.extend(d)
            view.append(prod(e.size for e in d))
            needs_view = True

    permute = list(range(len(levels)))
    nflat = len(flat_dims)
    for i, d in enumerate(flat_dims):
        try:
            idx = levels.index(d)
        except ValueError as e:
            raise DimensionBindError(
                f"tensor of dimensions {self.dims} does not contain dim {d}"
            ) from e
        p = permute[idx]
        del levels[idx]
        del permute[idx]
        levels.insert(i, 0)
        permute.insert(i, p)
    ptensor = ptensor.permute(*permute)
    seen = 0
    for i in range(len(levels) - 1, -1, -1):
        if isinstance(levels[i], int):
            seen += 1
            levels[i] = -seen
    result = Tensor.from_positional(ptensor, levels, self._has_device)
    if needs_view:
        result = result.reshape(*view, *result.size()[len(flat_dims) :])
    return result


def _contains_dim(input):
    from . import Dim

    for i in input:
        if isinstance(i, Dim):
            return True


def expand(self, *sizes):
    if not _contains_dim(sizes):
        return self.__torch_function__(torch.Tensor.expand, None, (self, *sizes))
    dims = sizes
    sizes = [d.size for d in dims] + [-1] * self.ndim
    self = self.expand(*sizes)
    return self[dims]


_not_present = object()


def _getarg(name, offset, args, kwargs, default):
    if len(args) > offset:
        return args[offset]
    return kwargs.get(name, default)


def _patcharg(name, offset, args, kwargs, value):
    if len(args) > offset:
        args[offset] = value
    else:
        kwargs[name] = value


def _wrap(
    orig, dim_offset=0, keepdim_offset=1, dim_name="dim", single_dim=False, reduce=True
):
    from . import Dim, Tensor, TensorLike

    def fn(self, *args, **kwargs):
        dim = _getarg(dim_name, dim_offset, args, kwargs, _not_present)
        if dim is _not_present or (single_dim and not isinstance(dim, Dim)):
            with _enable_layers(self.dims):
                print(f"dim fallback batch_tensor for {orig}")
                return Tensor.from_batched(
                    orig(self._batchtensor, *args, **kwargs), self._has_device
                )
        keepdim = (
            _getarg("keepdim", keepdim_offset, args, kwargs, False) if reduce else False
        )
        t, levels = self._tensor, llist(self._levels)
        dims = _dims(dim, self._batchtensor.ndim, keepdim, single_dim)
        dim_indices = tuple(levels.index(d) for d in dims)
        if reduce and not keepdim:
            new_levels = [l for i, l in enumerate(levels) if i not in dim_indices]
        else:
            new_levels = levels

        if len(dim_indices) == 1:
            dim_indices = dim_indices[
                0
            ]  # so that dims that really only take a single argument work...
        args = list(args)
        _patcharg(dim_name, dim_offset, args, kwargs, dim_indices)

        def wrap(t):
            if isinstance(t, TensorLike):
                return Tensor.from_positional(t, new_levels, self._has_device)
            return t

        with _enable_layers(new_levels):
            print(f"dim used batch_tensor for {orig}")
            r = orig(t, *args, **kwargs)
            return tree_map(wrap, r)

    return fn


def _def(name, *args, **kwargs):
    from . import _Tensor

    orig = getattr(torch.Tensor, name)
    setattr(_Tensor, name, _wrap(orig, *args, **kwargs))


no_slice = slice(None)

_orig_getitem = torch.Tensor.__getitem__


class dim_tracker:
    def __init__(self):
        self.dims = llist()
        self.count = []

    def record(self, d):
        if d not in self.dims:
            self.dims.append(d)
            self.count.append(1)

    def __getitem__(self, d):
        return self.count[self.dims.index(d)]


def t__getitem__(self, input):
    from . import _Tensor, Dim, DimensionBindError, DimList, Tensor, TensorLike

    # * bail to original example if we have a single non-Dim tensor, or a non-tensor
    # * locate ... or an unbound tensor list, and determine its size, bind dim list
    #   (remember that None does not count to the total dim count)
    # * bind simple dims and dim-packs to their sizes, count the number of uses of each dim,
    #   produce the re-view if needed
    # * for each single-use dim index, replace with no_slice and mark that it will be added
    #   (keep track of whether we have to call super)
    # * call super if needed
    # * if we have dims to bind, bind them (it will help if we eliminated ... and None before)

    # this handles bool indexing handling, as well as some other simple cases.

    is_simple = (
        not isinstance(input, Dim)
        and not isinstance(input, (tuple, list))
        and
        # WAR for functorch bug where zero time tensors in getitem are not handled correctly.
        not (isinstance(input, TensorLike) and input.ndim == 0)
    )

    if is_simple:
        if isinstance(self, _Tensor):
            return _Tensor.__torch_function__(_orig_getitem, None, (self, input))
        else:
            return _orig_getitem(self, input)

    # can further optimize this case
    if not isinstance(input, tuple):
        input = [input]
    else:
        input = list(input)

    dims_indexed = 0
    expanding_object = None
    dimlists = []
    for i, s in enumerate(input):
        if s is ... or isinstance(s, DimList) and not s.is_bound:
            if expanding_object is not None:
                msg = (
                    "at most one ... or unbound dimension list can exist in indexing list but"
                    f" found 2 at offsets {i} and {expanding_object}"
                )
                raise DimensionBindError(msg)
            expanding_object = i

        if isinstance(s, DimList):
            dims_indexed += len(s) if s.is_bound else 0
            dimlists.append(i)
        elif s is not None and s is not ...:
            dims_indexed += 1

    ndim = self.ndim
    if dims_indexed > ndim:
        raise IndexError(
            f"at least {dims_indexed} indices were supplied but the tensor only has {ndim} dimensions."
        )
    if expanding_object is not None:
        expanding_ndims = ndim - dims_indexed
        obj = input[expanding_object]
        if obj is ...:
            input[expanding_object : expanding_object + 1] = [
                no_slice
            ] * expanding_ndims
        else:
            obj.bind_len(expanding_ndims)
    # flatten the dimslists into the indexing
    for i in reversed(dimlists):
        input[i : i + 1] = input[i]
    dims_indexed = 0
    requires_view = False
    size = self.size()
    view_sizes = []
    dims_seen = dim_tracker()

    def add_dims(t):
        if not isinstance(t, _Tensor):
            return
        for d in t.dims:
            dims_seen.record(d)

    add_dims(self)
    dim_packs = []
    for i, idx in enumerate(input):
        if idx is None:
            input[i] = no_slice
            view_sizes.append(1)
            requires_view = True
        else:
            sz = size[dims_indexed]
            if isinstance(idx, Dim):
                idx.size = sz
                dims_seen.record(idx)
                view_sizes.append(sz)
            elif isinstance(idx, (tuple, list)) and idx and isinstance(idx[0], Dim):
                for d in idx:
                    dims_seen.record(idx)
                _bind_dims_to_size(sz, idx, f"offset {i}")
                view_sizes.extend(d.size for d in idx)
                requires_view = True
                dim_packs.append(i)
            else:
                add_dims(idx)
                view_sizes.append(sz)
            dims_indexed += 1
    if requires_view:
        self = self.view(*view_sizes)
    for i in reversed(dim_packs):
        input[i : i + 1] = input[i]

    # currenty:
    # input is flat, containing either Dim, or Tensor, or something valid for standard indexing
    # self may have first-class dims as well.

    # to index:
    # drop the first class dims from self, they just become direct indices of their positions

    # figure out the dimensions of the indexing tensors: union of all the dims in the tensors in the index.
    # these dimensions will appear and need to be bound at the first place tensor occures

    if isinstance(self, _Tensor):
        ptensor_self, levels = self._tensor, list(self._levels)
        # indices to ptensor rather than self which has first-class dimensions
        input_it = iter(input)
        flat_inputs = [next(input_it) if isinstance(l, int) else l for l in levels]
        has_device = self._has_device
        to_pad = 0
    else:
        ptensor_self, flat_inputs = self, input
        to_pad = ptensor_self.ndim - len(flat_inputs)
        has_device = True

    result_levels = []
    index_levels = []
    tensor_insert_point = None
    to_expand = {}
    requires_getindex = False
    for i, inp in enumerate(flat_inputs):
        if isinstance(inp, Dim) and dims_seen[inp] == 1:
            flat_inputs[i] = no_slice
            result_levels.append(inp)
        elif isinstance(inp, TensorLike):
            requires_getindex = True
            if tensor_insert_point is None:
                tensor_insert_point = len(result_levels)
            ptensor, levels, _ = _tensor_levels(inp)
            to_expand[i] = levels
            flat_inputs[i] = ptensor
            for l in levels:
                if l not in index_levels:
                    index_levels.append(l)
        else:
            requires_getindex = True
            result_levels.append(0)

    if tensor_insert_point is not None:
        result_levels[tensor_insert_point:tensor_insert_point] = index_levels

    for i, levels in to_expand.items():
        flat_inputs[i] = _match_levels(flat_inputs[i], levels, index_levels)

    if requires_getindex:
        result = _orig_getitem(ptensor_self, flat_inputs)
    else:
        result = ptensor_self

    next_positional = -1
    if to_pad > 0:
        result_levels.extend([0] * to_pad)
    for i, r in enumerate(reversed(result_levels)):
        if isinstance(r, int):
            result_levels[-1 - i] = next_positional
            next_positional -= 1

    return Tensor.from_positional(result, result_levels, has_device)


# XXX - dim is optional and can be the outer-most dimension...
def stack(tensors, new_dim, dim=0, out=None):
    if isinstance(dim, int):
        return torch.stack(tensors, dim, out).index(dim, new_dim)
    index = None
    if out is not None:
        out, index = _positional_no_permute(out, dim, expand_dim=True)
    ptensors = []
    for t in tensors:
        pt, pi = _positional_no_permute(t, dim, expand_dim=True)
        if index is not None and pi != index:
            pt = pt.move_dim(pi, index)
        else:
            index = pi
        ptensors.append(pt)
    pr = torch.stack(ptensors, index, out=out)
    return pr.index((index, index + 1), (new_dim, dim))


_orig_split = torch.Tensor.split


def split(self, split_size_or_sections, dim=0):
    from . import _Tensor, Dim

    if isinstance(split_size_or_sections, int) or any(
        isinstance(t, int) for t in split_size_or_sections
    ):
        if isinstance(dim, Dim):
            raise ValueError(
                "when dim is specified as a Dim object, split sizes must also be dimensions."
            )
        return _orig_split(self, split_size_or_sections, dim=dim)

    if isinstance(dim, Dim):
        assert isinstance(self, _Tensor), f"Tensor does not have dimension {dim}"
        self, dim = _positional_no_permute(self, dim)

    size = self.size(dim)
    total_bound_size = 0
    unbound = []
    sizes = []
    for i, d in enumerate(split_size_or_sections):
        if d.is_bound:
            sizes.append(d.size)
            total_bound_size += d.size
        else:
            sizes.append(0)
            unbound.append(i)

    if unbound:
        assert (
            total_bound_size <= size
        ), f"result dimensions are larger than original: {total_bound_size} vs {size} ({split_size_or_sections})"
        remaining_size = size - total_bound_size
        chunk_size = -(-remaining_size // len(unbound))
        for u in unbound:
            sz = min(chunk_size, remaining_size)
            split_size_or_sections[u].size = sz
            sizes[u] = sz
            remaining_size -= sz
    else:
        assert (
            total_bound_size == size
        ), f"result dimensions do not match original: {total_bound_size} vs {size} ({split_size_or_sections})"
    return tuple(
        t.index(dim, d)
        for d, t in zip(split_size_or_sections, _orig_split(self, sizes, dim=dim))
    )