mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-09-23 13:36:20 +00:00
5a419926b0
* convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
226 lines
8.9 KiB
Python
226 lines
8.9 KiB
Python
from __future__ import annotations
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from abc import ABC, ABCMeta, abstractmethod
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import logging
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from typing import Any, Callable
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from collections import deque
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import numpy as np
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from numpy.typing import DTypeLike
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logger = logging.getLogger(__name__)
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class LazyMeta(ABCMeta):
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def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs):
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def __getattr__(self, __name: str) -> Any:
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meta_attr = getattr(self._meta, __name)
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if callable(meta_attr):
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return type(self)._wrap_fn(
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(lambda s, *args, **kwargs: getattr(s, __name)(*args, **kwargs)),
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use_self=self,
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)
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elif isinstance(meta_attr, self._tensor_type):
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# e.g. self.T with torch.Tensor should still be wrapped
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return type(self)._wrap_fn(lambda s: getattr(s, __name))(self)
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else:
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# no need to wrap non-tensor properties,
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# and they likely don't depend on the actual contents of the tensor
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return meta_attr
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namespace["__getattr__"] = __getattr__
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# need to make a builder for the wrapped wrapper to copy the name,
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# or else it fails with very cryptic error messages,
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# because somehow the same string would end up in every closures
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def mk_wrap(op_name: str, *, meta_noop: bool = False):
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# need to wrap the wrapper to get self
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def wrapped_special_op(self, *args, **kwargs):
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return type(self)._wrap_fn(
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getattr(type(self)._tensor_type, op_name),
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meta_noop=meta_noop,
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)(self, *args, **kwargs)
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return wrapped_special_op
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# special methods bypass __getattr__, so they need to be added manually
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# ref: https://docs.python.org/3/reference/datamodel.html#special-lookup
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# NOTE: doing this from a metaclass is very convenient
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# TODO: make this even more comprehensive
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for binary_op in (
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"lt", "le", "eq", "ne", "ge", "gt", "not"
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"abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul",
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"neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor",
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"iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor",
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"radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor",
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):
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attr_name = f"__{binary_op}__"
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# the result of these operators usually has the same shape and dtype as the input,
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# so evaluation on the meta tensor can be skipped.
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namespace[attr_name] = mk_wrap(attr_name, meta_noop=True)
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for special_op in (
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"getitem", "setitem", "len",
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):
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attr_name = f"__{special_op}__"
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namespace[attr_name] = mk_wrap(attr_name, meta_noop=False)
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return super().__new__(cls, name, bases, namespace, **kwargs)
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# Tree of lazy tensors
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class LazyBase(ABC, metaclass=LazyMeta):
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_tensor_type: type
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_meta: Any
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_data: Any | None
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_lazy: deque[LazyBase] # shared within a graph, to avoid deep recursion when making eager
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_args: tuple
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_func: Callable[[tuple], Any] | None
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def __init__(self, *, meta: Any, data: Any | None = None, lazy: deque[LazyBase] | None = None, args: tuple = (), func: Callable[[tuple], Any] | None = None):
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super().__init__()
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self._meta = meta
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self._data = data
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self._lazy = lazy if lazy is not None else deque()
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self._args = args
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self._func = func
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assert self._func is not None or self._data is not None
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if self._data is None:
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self._lazy.append(self)
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def __init_subclass__(cls) -> None:
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if "_tensor_type" not in cls.__dict__:
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raise TypeError(f"property '_tensor_type' must be defined for {cls!r}")
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return super().__init_subclass__()
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@staticmethod
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def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any:
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# TODO: dict and set
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if isinstance(o, (list, tuple)):
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L = []
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for item in o:
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L.append(LazyBase._recurse_apply(item, fn))
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if isinstance(o, tuple):
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L = tuple(L)
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return L
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elif isinstance(o, LazyBase):
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return fn(o)
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else:
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return o
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@classmethod
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def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike = False) -> Callable[[Any], Any]:
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def wrapped_fn(*args, **kwargs):
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if kwargs is None:
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kwargs = {}
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args = ((use_self,) if use_self is not None else ()) + args
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meta_args = LazyBase._recurse_apply(args, lambda t: t._meta)
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if isinstance(meta_noop, bool) and not meta_noop:
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try:
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res = fn(*meta_args, **kwargs)
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except NotImplementedError:
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# running some operations on PyTorch's Meta tensors can cause this exception
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res = None
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else:
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# some operators don't need to actually run on the meta tensors
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assert len(args) > 0
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res = args[0]
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assert isinstance(res, cls)
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res = res._meta
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# allow operations to override the dtype
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if meta_noop is not True:
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res = cls.meta_with_dtype(res, meta_noop)
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if isinstance(res, cls._tensor_type):
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def collect_replace(t: LazyBase):
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if collect_replace.shared_lazy is None:
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collect_replace.shared_lazy = t._lazy
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else:
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collect_replace.shared_lazy.extend(t._lazy)
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t._lazy = collect_replace.shared_lazy
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# emulating a static variable
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collect_replace.shared_lazy = None
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LazyBase._recurse_apply(args, collect_replace)
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shared_lazy = collect_replace.shared_lazy
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return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs))
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else:
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del res # not needed
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# non-tensor return likely relies on the contents of the args
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# (e.g. the result of torch.equal)
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eager_args = cls.to_eager(args)
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return fn(*eager_args, **kwargs)
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return wrapped_fn
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@classmethod
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def to_eager(cls, t: Any) -> Any:
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def simple_to_eager(_t: LazyBase) -> Any:
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def already_eager_to_eager(_t: LazyBase) -> Any:
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assert _t._data is not None
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return _t._data
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while _t._data is None:
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lt = _t._lazy.popleft()
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if lt._data is not None:
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raise ValueError(f"{lt} did not belong in the lazy queue")
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assert lt._func is not None
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lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
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lt._data = lt._func(lt._args)
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# sanity check
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assert lt._data.dtype == lt._meta.dtype
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assert lt._data.shape == lt._meta.shape
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return _t._data
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# recurse into lists and/or tuples, keeping their structure
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return cls._recurse_apply(t, simple_to_eager)
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@classmethod
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def eager_to_meta(cls, t: Any) -> Any:
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return cls.meta_with_dtype(t, t.dtype)
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# must be overridden, meta tensor init is backend-specific
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@classmethod
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@abstractmethod
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def meta_with_dtype(cls, m: Any, dtype: Any) -> Any: pass
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@classmethod
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def from_eager(cls, t: Any) -> Any:
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if type(t) is cls:
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# already eager
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return t
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elif isinstance(t, cls._tensor_type):
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return cls(meta=cls.eager_to_meta(t), data=t)
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else:
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return TypeError(f"{type(t)!r} is not compatible with {cls._tensor_type!r}")
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class LazyNumpyTensor(LazyBase):
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_tensor_type = np.ndarray
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@classmethod
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def meta_with_dtype(cls, m: np.ndarray[Any, Any], dtype: DTypeLike) -> np.ndarray[Any, Any]:
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# The initial idea was to use np.nan as the fill value,
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# but non-float types like np.int16 can't use that.
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# So zero it is.
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cheat = np.zeros(1, dtype)
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return np.lib.stride_tricks.as_strided(cheat, m.shape, (0 for _ in m.shape))
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def astype(self, dtype, *args, **kwargs):
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meta = type(self).meta_with_dtype(self._meta, dtype)
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full_args = (self, dtype,) + args
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# very important to pass the shared _lazy deque, or else there's an infinite loop somewhere.
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return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs)))
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def tofile(self, *args, **kwargs):
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eager = LazyNumpyTensor.to_eager(self)
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return eager.tofile(*args, **kwargs)
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# TODO: __array_function__
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