mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-04-07 03:00:26 -04:00
4812801aa6
* try for canonical order * cmp better * disable bad tests * flip const order * fix test * fix tests * different fix for NOOP * metaclass here * fix tests * narrower scope
349 lines
19 KiB
Python
349 lines
19 KiB
Python
from __future__ import annotations
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import functools, operator, itertools, math
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from dataclasses import dataclass
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from typing import Tuple, List, Optional, Dict, Set, cast
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from tinygrad.dtype import dtypes
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from tinygrad.ops import resolve, UOp, Variable, sint, sym_infer, smax, smin
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from tinygrad.helpers import prod, all_int, argsort, flatten
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@functools.lru_cache(maxsize=None)
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def canonicalize_strides(shape:Tuple[sint, ...], strides:Tuple[sint, ...]) -> Tuple[sint, ...]:
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return tuple(0 if s == 1 else st for s, st in zip(shape, strides))
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@functools.lru_cache(maxsize=None)
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def strides_for_shape(shape:Tuple[sint, ...]) -> Tuple[sint, ...]:
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if not shape: return ()
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strides = tuple(itertools.accumulate(reversed(shape[1:]), operator.mul, initial=1))[::-1]
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return canonicalize_strides(shape, strides)
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@functools.lru_cache(maxsize=None)
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def _merge_dims(shape:Tuple[int, ...], strides:Tuple[int, ...], mask:Optional[Tuple[Tuple[int, int], ...]]=None) -> Tuple[Tuple[int, int, int], ...]:
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# merge contiguous sub-parts or zero strided dims. ret = Tuple[(merged_size, stride, merged size w/o zero stride), ...]
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if not shape: return ()
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assert len(shape) == len(strides) and (mask is None or len(shape) == len(mask))
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ret = [(shape[0], strides[0], shape[0] if strides[0] != 0 else 0)]
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# merge this dim to next dim if size is 1
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merging = (mask[0][1] - mask[0][0] == 1) if mask is not None else shape[0] == 1
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for i, (s, st) in enumerate(zip(shape[1:], strides[1:]), start=1):
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last_s, last_st, last_pre_expand_s = ret[-1]
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# always merge 1
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if s == 1: continue
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# merge last dim with this dim if merging or strides matched
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if merging or last_st == s * st: ret[-1] = (last_s * s, st, (s if merging else last_pre_expand_s * s) if st != 0 else 0)
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else: ret.append((s, st, s if st != 0 else 0))
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# merge this dim to next dim if size is 1
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merging = (mask[i][1] - mask[i][0] == 1) if mask is not None else s == 1
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return tuple(ret)
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@functools.lru_cache(maxsize=None)
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def _reshape_mask(_mask:Optional[Tuple[Tuple[sint, sint], ...]], old_shape:Tuple[sint, ...], new_shape:Tuple[sint, ...]) \
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-> Optional[Tuple[Tuple[sint, sint], ...]]:
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"""Returns the new mask if reshape is possible, and None if not possible."""
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if _mask is None: return tuple((0, s) for s in new_shape)
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if any(not isinstance(m[0], int) or not isinstance(m[1], int) for m in _mask): return None
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if any(m[1] - m[0] < 1 for m in _mask): return ((0, 0),) * len(new_shape) # zero mask
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new_mask: List[Tuple[int, int]] = []
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# _mask is all int here
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r_masks, r_shape, r_new_shape = reversed(cast(Tuple[Tuple[int, int], ...], _mask)), reversed(old_shape), reversed(new_shape)
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curr_stride, old_dim, new_dim, mask = 1, next(r_shape, 1), next(r_new_shape, 1), next(r_masks, (0,1))
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while len(new_mask) < len(new_shape):
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(l, r), next_stride = mask, new_dim * curr_stride
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if old_dim >= next_stride: # need to split mask.
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if old_dim == next_stride: # simply copy the mask and get next batch for merging
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new_mask.append((l // curr_stride, (r - 1) // curr_stride + 1))
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curr_stride, old_dim, new_dim, mask = 1, next(r_shape, 1), next(r_new_shape, 1), next(r_masks, (0,1))
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else: # mask can only be splitted if reshape doesn't cut across the mask.
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if (((l % next_stride != 0 or r % next_stride != 0) and l // next_stride != (r - 1) // next_stride)
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or old_dim % next_stride != 0): return None
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new_mask.append((l % next_stride // curr_stride, (r - 1) % next_stride // curr_stride + 1))
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curr_stride, new_dim = next_stride, next(r_new_shape, 1) # need to get mask for next dimension
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else:
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next_mask = next(r_masks, (0, 1))
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# combine if the mask can unfold continuously
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if mask != (0, old_dim) and next_mask[1] - next_mask[0] != 1: return None
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mask, old_dim = (next_mask[0] * old_dim + l, (next_mask[1] - 1) * old_dim + r), old_dim * next(r_shape, 1)
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for mask in r_masks: # if the old shape has leading 1s, need to make sure their mask is (0,1)
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if mask != (0, 1): return ((0, 0),) * len(new_shape) # invalid mask
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return tuple(reversed(new_mask))
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def un1d(shape:Tuple[sint, ...], offs:sint) -> List[sint]:
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strides = strides_for_shape(shape)
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result = []
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for stride in strides:
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here = offs // stride if stride != 0 else 0
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result.append(here)
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offs -= here * stride
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return result
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def variable_to_uop(x, ctx=None) -> UOp: return UOp.const(dtypes.int, x) if isinstance(x, int) else x
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@dataclass(frozen=True)
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class View:
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shape:Tuple[sint, ...]
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strides:Tuple[sint, ...]
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offset:sint
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mask:Optional[Tuple[Tuple[sint, sint], ...]]
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contiguous:bool
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@functools.cached_property
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def t(self):
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return tuple(x.tuplize if isinstance(x, UOp) else (x,) \
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for x in self.shape+self.strides+(self.offset,)+(tuple(flatten(self.mask)) if self.mask is not None else tuple()))
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def __lt__(self, o:View): return self.t < o.t
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def to_indexed_uops(self:View, _idxs:Optional[List[UOp]]=None, vexpr:UOp=UOp.const(dtypes.bool, True)) -> Tuple[UOp, UOp]:
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idxs = [UOp.range(dtypes.int, 0, s, i) for i,s in enumerate(self.shape)] if _idxs is None else _idxs
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iexpr = variable_to_uop(self.offset)
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for idx,sh,st,m in zip(idxs, self.shape, self.strides, self.mask if self.mask is not None else [None]*len(self.shape)):
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if resolve(sh != 1) and resolve(st != 0): iexpr = iexpr + idx*st
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if m is not None:
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if resolve(m[0] != 0): vexpr = vexpr * idx.ge(m[0])
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if resolve(m[1] != sh): vexpr = vexpr * idx.lt(m[1])
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return iexpr, vexpr
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def size(self) -> int:
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ret = prod([x.vmax if isinstance(x, UOp) else x for x in self.shape])
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assert isinstance(ret, int), f"{ret=} is not int"
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return ret
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@staticmethod
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@functools.lru_cache(maxsize=None)
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def create(shape:Tuple[sint, ...], strides:Optional[Tuple[sint, ...]]=None, offset:sint=0, mask:Optional[Tuple[Tuple[sint, sint], ...]]=None):
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# TODO: this resolve shouldn't be needed
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if not all(resolve(s >= 0) for s in shape): raise ValueError(f"Trying to create View with negative dimension: {shape=}")
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strides = canonicalize_strides(shape, strides) if strides else strides_for_shape(shape)
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# canonicalize 0 in shape
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if 0 in shape: return View(shape, (0,) * len(shape), offset=0, mask=None, contiguous=True)
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# canonicalize empty mask
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if mask is not None and all(m == (0,s) for m,s in zip(mask, shape)): mask = None
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# if any dimension has size >1, but is masked such that only one index in the dimension is unmasked
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# then its stride can also be set to 0, albeit with a corresponding adjustment required to the offset
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if mask and any(elim := [not resolve(b+1 < e) for b,e in mask]):
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if any(not resolve(b < e) for b,e in mask):
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strides, offset, mask = (0,) * len(shape), 0, ((0,0),) * len(shape)
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offset += sum((strides[i] * mask[i][0]) if e else 0 for i, e in enumerate(elim))
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strides = tuple(0 if e else st for st,e in zip(strides, elim))
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# simplify as we go
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if isinstance(offset, UOp): offset = cast(sint, offset.ssimplify())
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shape = tuple(cast(sint, x.ssimplify()) if isinstance(x, UOp) else x for x in shape)
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# TODO: enabling stride simplification breaks it
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"""
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strides = tuple(x.ssimplify() if isinstance(x, UOp) else x for x in strides)
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if mask: mask = tuple((s.ssimplify() if isinstance(s, UOp) else s, e.ssimplify() if isinstance(e, UOp) else e) for s,e in mask)
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"""
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contiguous = offset == 0 and mask is None and strides == strides_for_shape(shape)
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return View(shape, strides, offset, mask, contiguous)
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@functools.lru_cache(None) # pylint: disable=method-cache-max-size-none
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def vars(self) -> Set[Variable]:
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flatten_mask = tuple(x for m in self.mask for x in m) if self.mask is not None else tuple()
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return functools.reduce(operator.or_, [x.vars() for x in self.shape+self.strides+(self.offset,)+flatten_mask if isinstance(x, UOp)], set())
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@functools.lru_cache(None) # pylint: disable=method-cache-max-size-none
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def unbind(self) -> Tuple[View, Dict[Variable, int]]:
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var_unboundvar_val = [(v, v.unbind()) for v in self.vars()]
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unbound_vars = {v:uv for v,(uv,_) in var_unboundvar_val}
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def substitute(x): return x if isinstance(x, int) else x.substitute(unbound_vars)
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new_shape = tuple(map(substitute, self.shape))
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new_strides = tuple(map(substitute, self.strides))
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new_offset = substitute(self.offset)
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new_mask = tuple((substitute(x[0]), substitute(x[1])) for x in self.mask) if self.mask is not None else None
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return View.create(new_shape, new_strides, new_offset, new_mask), dict(x[1] for x in var_unboundvar_val)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def __add__(self, vm1:View) -> Optional[View]:
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vm2 = self
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if vm2.contiguous: return vm1
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if vm1.contiguous and vm1.shape == vm2.shape: return vm2
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if vm1.contiguous and vm1.size() == vm2.size() and (ret := vm2.reshape(vm1.shape)) is not None: return ret
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if vm1.mask:
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for b,e in vm1.mask:
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if resolve(b >= e, False): return View.create(vm1.shape, (0,) * len(vm1.shape), 0, ((0,0),) * len(vm1.shape))
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return (merged := vm2 + vm1.shrink(vm1.mask)) and merged.pad(tuple((b,s-e) for (b,e),s in zip(vm1.mask, vm1.shape)))
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# Project vm1's offset and strides on to vm2.
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origin = un1d(vm2.shape, vm1.offset)
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terms: List[List[Tuple[int, sint]]] = [[] for _ in origin]
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strides: List[sint] = [0] * len(vm1.shape)
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for d1, st in enumerate(vm1.strides):
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if st == 0: continue
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for d2, (o, s1) in enumerate(zip(origin, un1d(vm2.shape, vm1.offset + st))):
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if (s1 := s1 - o) == 0: continue
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terms[d2].append((d1, s1))
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strides[d1] += s1 * vm2.strides[d2]
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# Merge dimensions in vm2 if required.
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# NB: Merging too many dimensions can make it difficult to project vm2's mask, hence only combining when required.
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if not all_int(vm1.shape): return None
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idxs: List[UOp] = [UOp.variable(f"idx{i}", 0, s-1) for i,s in enumerate(vm1.shape)]
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merged_size, merged_term = 1, UOp.const(dtypes.int, 0)
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extents: List[Tuple[sint, UOp]] = []
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for term, s, o in zip(reversed(terms), reversed(vm2.shape), reversed(origin)):
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merged_term += (sum([idxs[d1] * s1 for d1, s1 in term]) + o) * merged_size
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merged_size *= s
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if resolve(merged_term < merged_size, False) and resolve(0 <= merged_term, False):
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extents.append((merged_size, merged_term))
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merged_size, merged_term = 1, UOp.const(dtypes.int, 0)
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if resolve(merged_term != 0): return None
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if (vm2_shape := tuple(s for s,_ in reversed(extents))) != vm2.shape:
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return (reshaped_vm2 := vm2.reshape(vm2_shape)) and reshaped_vm2 + vm1
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if vm2.mask:
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# Try to project vm2's mask on to vm1.
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newb, newe, bad = [0] * len(vm1.shape), list(vm1.shape), False
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for (b, e), o, term, (_, t) in zip(vm2.mask, origin, terms, reversed(extents)):
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if resolve(b <= t.vmin and t.vmax < e, False): continue
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if not all_int([o, b, e]):
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bad = True
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continue
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if len(term) != 1:
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if not term and newe: newe[0] = 0
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else: bad = True
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continue
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d1, s1 = term[0]
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if not isinstance(s1, int) or not isinstance(newe[d1], int):
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bad = True
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continue
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newb[d1] = max(newb[d1], math.ceil((b - o if s1 > 0 else e - o - 1) / s1))
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newe[d1] = min(newe[d1], (b - o if s1 < 0 else e - o - 1) // s1 + 1)
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# If any of vm1 was masked off, try again with that mask in place.
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for b, e, s in zip(newb, newe, vm1.shape):
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if (b, e) != (0, s):
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return vm2 + View.create(vm1.shape, vm1.strides, vm1.offset, tuple(zip(newb, newe)))
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# Otherwise if vm2's mask was violated, then cannot merge.
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if bad: return None
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return View.create(vm1.shape, tuple(strides), sum(o * s for o, s in zip(origin, vm2.strides)) + vm2.offset)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def invert(self, out_shape:Tuple[sint, ...]) -> Optional[View]:
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ret = View.create(self.shape)
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if self.mask: ret = ret.shrink(self.mask)
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ret = ret.stride(tuple(-1 if x < 0 else 1 for x in self.strides)).permute(argsort(tuple(-x if x > 0 else x for x in self.strides)))
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return ret if prod(ret.shape) == prod(out_shape) else None # don't support shrink, expand, or stride != (-1, 1)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def minify(self):
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min_shape = tuple(x[0] for x in _merge_dims(self.shape, self.strides, self.mask))
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return nv if (nv := self.reshape(min_shape)) else self
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def __unsafe_resize(self, arg: Tuple[Tuple[sint, sint], ...], mask=None) -> View:
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offset = sum([s * x[0] for s, x in zip(self.strides,arg)])
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if self.mask:
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# move the old mask
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nmask = tuple([(smax(0, smin(mx-ax,ay-ax)), smax(0, smin(my-ax,ay-ax))) for (mx,my),(ax,ay) in zip(self.mask, arg)])
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# merge the masks if we have two
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mask = tuple([(smax(mx1, mx2), smin(my1, my2)) for (mx1, my1), (mx2, my2) in zip(nmask, mask)]) if mask is not None else nmask
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shape = [y-x for x,y in arg]
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if mask is not None and all(m[0] == 0 and m[1] == s for m,s in zip(mask, shape)): mask = None
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return View.create(tuple(s.ssimplify() if isinstance(s, UOp) else s for s in shape), self.strides, self.offset+offset, mask)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def pad(self, arg: Tuple[Tuple[sint, sint], ...]) -> View:
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assert len(arg) == len(self.shape), f"invalid pad {arg} for {self.shape}"
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# NOTE: not checking for symbolic arg
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for b,e in arg: assert not all_int([b,e]) or b>=0 and e>=0, f"invalid pad {arg} for {self.shape}"
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if any(resolve(b!=0) or resolve(e!=0) for b, e in arg):
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zvarg = tuple([(-b,s+e) for s,(b,e) in zip(self.shape, arg)])
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mask = tuple([(b,s+b) for s,(b,_) in zip(self.shape, arg)])
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return self.__unsafe_resize(zvarg, mask=mask)
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return self
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def shrink(self, arg: Tuple[Tuple[sint, sint], ...]) -> View:
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assert len(arg) == len(self.shape), f"invalid shrink {arg} for {self.shape}"
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# NOTE: not checking for symbolic arg
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for s,(b,e) in zip(self.shape,arg): assert not all_int([s,b,e]) or (0<=b<=e<=s), f"invalid shrink {arg} for {self.shape}"
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return self.__unsafe_resize(arg)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def expand(self, new_shape: Tuple[sint, ...]) -> View:
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if len(new_shape) != len(self.shape): raise ValueError(f"expand arg {new_shape=} must have same number of dimensions as shape {self.shape=}")
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if 0 in self.shape:
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assert all((s == x == 0) or (s > 0 and (x % s) == 0) for s,x in zip(self.shape, new_shape)), f"can't expand {self.shape} into {new_shape}"
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return View.create(new_shape)
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# TODO: this resolve might be wrong
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assert all((not resolve(s != x, False) or s == 1) for s,x in zip(self.shape, new_shape)), f"can't expand {self.shape} into {new_shape}"
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# NOTE: can the mask ever be (0,0)?
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# TODO: this resolve may not be needed, but it's hard because vars need to be sorted
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mask = tuple([(((0,0) if m != (0,1) else (0,ns)) if resolve(s != ns, False) else m) \
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for m,s,ns in zip(self.mask, self.shape, new_shape)]) if self.mask else None
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return View.create(new_shape, self.strides, self.offset, mask)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def permute(self, axis: Tuple[int, ...]) -> View:
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assert sorted(axis) == list(range(len(self.shape))), f"invalid permutation {axis} of len {len(self.shape)}"
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return View.create(tuple(self.shape[a] for a in axis), tuple(self.strides[a] for a in axis), self.offset,
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tuple(self.mask[a] for a in axis) if self.mask is not None else None)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def stride(self, mul: Tuple[int, ...]) -> View:
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# except for the negative case, you can build this from the others. invertible in the negative case
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assert all(isinstance(x, int) and x != 0 for x in mul), f"invalid stride {mul} for {self.shape}"
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strides = tuple([z*m for z,m in zip(self.strides, mul)])
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new_shape = tuple([(s+(abs(m)-1))//abs(m) for s,m in zip(self.shape, mul)])
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offset = sum([(s-1)*z for s,z,m in zip(self.shape, self.strides, mul) if m < 0])
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mask = tuple([(((mx if m > 0 else s-my)+(abs(m)-1))//abs(m), ((my if m > 0 else s-mx)+(abs(m)-1))//abs(m)) \
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for (mx,my),s,m in zip(self.mask, self.shape, mul)]) if self.mask is not None else None
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return View.create(new_shape, strides, self.offset + offset, mask)
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@functools.lru_cache(maxsize=None) # pylint: disable=method-cache-max-size-none
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def reshape(self, new_shape: Tuple[sint, ...]) -> Optional[View]:
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if self.shape == new_shape: return self
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# TODO: this resolve shouldn't be needed
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assert all(resolve(x >= 0) for x in new_shape), f"shape can't contain negative numbers {new_shape}"
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if 0 in self.shape:
|
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assert 0 in new_shape, f"cannot reshape 0 size to {new_shape}"
|
|
return View.create(new_shape)
|
|
# check for the same size
|
|
if (self_all_int := all_int(self.shape)):
|
|
assert all(isinstance(s, (int, UOp)) for s in new_shape), f"{self.shape=} -> {new_shape=} contains non (int, Variable) dim"
|
|
if resolve(prod(self.shape) != prod(new_shape), False):
|
|
raise ValueError(f"size mismatched, can't reshape {self.shape=} -> {new_shape=}")
|
|
|
|
if new_shape == () and self.mask and any(mx==my for (mx,my) in self.mask): return None
|
|
|
|
# after the asserts, it's okay to check contiguous
|
|
if self.contiguous: return View.create(new_shape)
|
|
|
|
# if it's not contiguous and new shape is symbolic, check if it's directly replaceable
|
|
if self_all_int and not all_int(new_shape):
|
|
if len(self.shape) != len(new_shape): raise ValueError(f"cannot symbolic reshape non-contiguous {self} -> {new_shape}")
|
|
for si, so in zip(self.shape, new_shape):
|
|
if isinstance(so, int):
|
|
if si != so: raise ValueError(f"cannot symbolic reshape non-contiguous {self} -> {new_shape}")
|
|
else:
|
|
var_vals = dict([v.unbind() for v in so.vars()])
|
|
if si != sym_infer(so, var_vals): raise ValueError(f"cannot symbolic reshape non-contiguous {self} -> {new_shape}")
|
|
# all dimensions matched, return the new view directly
|
|
return View(new_shape, self.strides, self.offset, self.mask, self.contiguous)
|
|
|
|
strides, r_new_shape = [], reversed(new_shape)
|
|
for merged_dim, new_stride, real_dim in reversed(_merge_dims(self.shape, self.strides, self.mask)):
|
|
acc = 1
|
|
# TODO: third resolve shouldn't be needed
|
|
while resolve(acc <= merged_dim) and resolve(acc != merged_dim) and resolve((new_dim := next(r_new_shape, 0)) > 0):
|
|
strides.append(new_stride)
|
|
if resolve(new_dim != 1): new_stride *= (new_dim if resolve((acc := acc * new_dim) < real_dim) else 0)
|
|
if resolve(acc != merged_dim): break
|
|
else:
|
|
strides += [0,] * (len(new_shape) - len(strides))
|
|
new_mask = _reshape_mask(self.mask, self.shape, new_shape)
|
|
if new_mask is not None:
|
|
new_strides = canonicalize_strides(tuple(e-b for b,e in new_mask), tuple(reversed(strides)))
|
|
extra_offset = (sum(m[0] * s for m,s in zip(self.mask, self.strides)) if self.mask else 0) - \
|
|
(sum(m[0] * s for m,s in zip(new_mask, new_strides)))
|
|
return View.create(new_shape, new_strides, self.offset + extra_offset, new_mask)
|
|
|
|
return None
|