import ctypes import numpy as np from collections import defaultdict, deque from typing import TypeVar, Type, Any, Dict, Deque, Tuple from tinygrad.helpers import DType, dtypes, prod, GlobalCounters, ImageDType _T = TypeVar("_T") class RawBuffer: # pylint: disable=abstract-method def __init__(self, size:int, dtype:DType, buf:Any=None, allocator:Any=None, **kwargs): self.size: int = size self.dtype: DType = dtype self._buf = buf if buf is not None else (allocator.alloc(size, dtype, **kwargs) if allocator else None) # If buf is provided, use it. Otherwise try to allocate from the allocator. self._memsz: int = size*dtype.itemsize self._allocator = allocator self._device = kwargs.get('device', None) GlobalCounters.mem_used += self._memsz def __del__(self): # NOTE: if it fails on init (bad dtype), it won't have a _memsz if hasattr(self, '_memsz'): GlobalCounters.mem_used -= self._memsz if hasattr(self, '_allocator') and self._allocator: self._allocator.free(self._buf) def __repr__(self): return f"buffer<{self.size}, {self.dtype}, {id(self)}>" @property def key(self): return (self.size, self.dtype) # NOTE: this interface allows for 0 copy @classmethod def fromCPU(cls:Type[_T], x:np.ndarray) -> _T: raise NotImplementedError("must be implemented") def toCPU(self) -> np.ndarray: raise NotImplementedError("must be implemented") class RawBufferCopyIn(RawBuffer): def _copyin(self, x:np.ndarray) -> None: raise NotImplementedError("must be implemented") @classmethod def fromCPU(cls, x:np.ndarray, **kwargs): ret = cls(prod(x.shape), dtypes.from_np(x.dtype), **kwargs) if x.size > 0: ret._copyin(x) return ret class RawBufferMapped(RawBufferCopyIn): def _buffer(self) -> memoryview: raise NotImplementedError("must be implemented") # NOTE: this metadata prevents the backing buffer from being freed. hack can be removed with PEP688 def buffer_view(self) -> np.ndarray: return np.frombuffer(self._buffer(), dtype=np.dtype(self.dtype.np, metadata={"backing": self}), count=self.size) # type: ignore def toCPU(self) -> np.ndarray: return self.buffer_view().copy() # Need a copy, since jit will write to the same buffer. def _copyin(self, x:np.ndarray) -> None: np.copyto(self.buffer_view(), x.reshape(-1)) # this one is simple enough that i moved it out of the runtimes class RawMallocBuffer(RawBufferMapped): def __init__(self, size, dtype: DType): super().__init__(size, dtype, ({dtypes.float64:ctypes.c_double, dtypes.float32: ctypes.c_float, dtypes.float16: ctypes.c_int16, dtypes.bfloat16: ctypes.c_int16, dtypes.int8: ctypes.c_int8, dtypes.uint8: ctypes.c_uint8, dtypes.bool: ctypes.c_uint8, dtypes.int32: ctypes.c_int32, dtypes.uint32: ctypes.c_uint32, dtypes.int64: ctypes.c_int64, dtypes.uint64: ctypes.c_uint64, dtypes.int16: ctypes.c_int16, dtypes.uint16: ctypes.c_uint16}[dtype] * size)()) def _buffer(self): return memoryview(self._buf) class RawBufferCopyInOut(RawBufferCopyIn): def _copyout(self, x:np.ndarray) -> None: raise NotImplementedError("must be implemented") def toCPU(self) -> np.ndarray: x: np.ndarray = np.empty(self.size, dtype=self.dtype.np) if x.size > 0: self._copyout(x) return x class RawBufferTransfer(RawBuffer): def _transfer(self, x) -> None: raise NotImplementedError("must be implemented") @classmethod def transfer(cls, x, shape, dtype, **kwargs): ret = cls(prod(shape), dtype, **kwargs) ret._transfer(x) return ret class LRUAllocator: def __init__(self, dev_memsz=(4<<30)): self.epoch = 0 self.free_space: Dict[Any, int] = defaultdict(lambda: dev_memsz) self.buffer_info: Dict[Any, Tuple[int, DType, str]] = dict() self.cached_buffers: Dict[Tuple[int, ...], Deque[Tuple[Any, int]]] = defaultdict(deque) # Cached buffer storage, splitted by type and size, newest first. self.aging_order: Dict[Any, Deque[Tuple[Tuple[int, ...], int]]] = defaultdict(deque) # Keys of cached_buffers, ordered from oldest to newest updates. def _cache_reuse_buffer(self, rawbufs: Deque[Tuple[Any, int]]): # The newest cached buffer is reused. GlobalCounters.mem_cached -= self._underlying_buf_memsz(rawbufs[0][0]) return rawbufs.popleft()[0] def ensure_has_free_space(self, size, dtype, device): while len(self.aging_order[device]) and (self.free_space[device]-size*dtype.itemsize) < 0: # When OOM removing lru buffers. bucket, epoch = self.aging_order[device].popleft() if self.cached_buffers[bucket] and self.cached_buffers[bucket][-1][1] == epoch: self._free_buffer(self.cached_buffers[bucket].pop()[0]) # Free cached buffer if it is still in cache. def _alloc_buffer(self, size, dtype, device, **kwargs): self.ensure_has_free_space(size, dtype, device) self.free_space[device] -= size*dtype.itemsize newbuf = self._do_alloc(max(1, size), dtype, device, **kwargs) self.buffer_info[newbuf] = (size, dtype, device) return newbuf def _free_buffer(self, buf_to_free): self.free_space[self.buffer_info[buf_to_free][2]] += self._underlying_buf_memsz(buf_to_free) GlobalCounters.mem_cached -= self._underlying_buf_memsz(buf_to_free) self.buffer_info.pop(buf_to_free) self._do_free(buf_to_free) def alloc(self, size, dtype, device='0', **kwargs): rawbufs = self.cached_buffers.get(self._cached_bufkey(size, dtype, device), None) return self._cache_reuse_buffer(rawbufs) if rawbufs else self._alloc_buffer(size, dtype, device, **kwargs) def free(self, buf): # free() just caches buffer. It might be freed later when OOM during allocation. self.epoch += 1 size, dtype, device = self.buffer_info[buf] self.cached_buffers[self._cached_bufkey(size, dtype, device)].appendleft((buf, self.epoch)) self.aging_order[device].append((self._cached_bufkey(size, dtype, device), self.epoch)) GlobalCounters.mem_cached += self._underlying_buf_memsz(buf) def _underlying_buf_memsz(self, buf): return self.buffer_info[buf][0] * self.buffer_info[buf][1].itemsize def _cached_bufkey(self, size, dtype, device) -> Tuple[int, ...]: return (device, size, dtype, dtype.shape) if isinstance(dtype, ImageDType) else (device, size, dtype) # Provides a key for reusing device buffers with identical keys. def _do_alloc(self, size, dtype, device, **kwargs): raise NotImplementedError("must be implemented") def _do_free(self, buf): pass