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139 lines
7.4 KiB
139 lines
7.4 KiB
# ShapeTracker allows movement operations to a buffer that don't require a copy to be made.
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from __future__ import annotations
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from dataclasses import dataclass
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import functools
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from typing import Optional, Callable
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from tinygrad.helpers import merge_dicts, getenv
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from tinygrad.shape.view import View, strides_for_shape, unravel
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from tinygrad.dtype import dtypes
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from tinygrad.ops import UOp, Ops, graph_rewrite, Variable, sint, sint_to_uop, Context, PatternMatcher, UPat, GroupOp
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from tinygrad.codegen.symbolic import split_uop, symbolic_flat, uop_given_valid, simplify_valid
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# If a node overflow, its srcs need to be checked to see if this overflow is the result of an ALU operation,
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# or that the node simply inherits the dtype from srcs. Upcast is either `Ops.CAST`+`replace` or just `replace`.
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def handle_upcast(u: UOp) -> UOp|None:
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dtype = dtypes.int64.vec(u.dtype.count) if u.dtype.count > 1 else dtypes.int64
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# check for overflow, upcast this to int64
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if u.vmax > dtypes.max(dtypes.int) or u.vmin < dtypes.min(dtypes.int):
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return u.replace(dtype=dtype, src=tuple([x.cast(dtype) for x in u.src]))
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# if any inputs are int64 and this *doesn't* overflow, cast back to int
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if any(x.dtype == dtypes.int64 for x in u.src):
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return u.replace(dtype=dtype, src=tuple([x.cast(dtype) for x in u.src])).cast(u.dtype)
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return None
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pm_upcast = PatternMatcher([(UPat(GroupOp.ALU, dtype=dtypes.int, name="u"), handle_upcast),])
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@functools.cache
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def views_to_indexed_uops(views: tuple[View, ...], _idxs:Optional[tuple[UOp, ...]]=None) -> tuple[UOp, UOp]:
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idx, valid = views[-1].to_indexed_uops(_idxs)
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for view in reversed(views[0:-1]):
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view = view.minify()
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idx, valid = view.to_indexed_uops([sint_to_uop(i) for i in unravel(view.shape, idx)], valid)
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# symbolic
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idx, valid = graph_rewrite(UOp.sink(idx, valid), symbolic_flat, name="indexing sym @ 1").src
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# simplify
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if (newvalid:=simplify_valid(valid)) is not None: valid = newvalid
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if (newidx:=uop_given_valid(valid, idx)) is not None: idx = newidx
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# symbolic again, upcast if needed
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return graph_rewrite(UOp.sink(idx, valid), symbolic_flat+pm_upcast, name="indexing sym @ 2").src
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@functools.cache
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def views_to_real_strides(views: tuple[View, ...], ignore_valid=False) -> tuple[Optional[sint], ...]:
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# NOTE: if a stride is not always valid, it will be None
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if len(views) == 1 and views[-1].mask is None: return views[-1].strides
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ret: list[Optional[sint]] = [None] * len(views[-1].shape)
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idx, valid = views_to_indexed_uops(views)
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for c in split_uop(idx, Ops.ADD):
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if c.op is Ops.RANGE: ret[c.arg] = 1
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if c.op is Ops.MUL and c.src[0].op is Ops.RANGE and c.src[1].op is Ops.CONST: ret[c.src[0].arg] = c.src[1].arg
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if c.op is Ops.MUL and c.src[1].op is Ops.RANGE and c.src[0].op is Ops.CONST: ret[c.src[1].arg] = c.src[0].arg
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used_ranges = [x.arg for x in idx.toposort() if x.op is Ops.RANGE]
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ret = [x if i in used_ranges else 0 for i,x in enumerate(ret)]
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if not ignore_valid:
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for masked_axis in [x.arg for x in valid.toposort() if x.op is Ops.RANGE]: ret[masked_axis] = None
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return tuple(ret)
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@dataclass(frozen=True, order=True)
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class ShapeTracker:
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views: tuple[View, ...]
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def __add__(self, st:ShapeTracker) -> ShapeTracker:
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ret = self
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for v in st.views: ret = ShapeTracker(ret.views + (v,)).simplify() # one view at a time = better simplification
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return ret
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def invert(self, out_shape:tuple[sint, ...]) -> Optional[ShapeTracker]:
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inverted_views:list[View] = []
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for v,s in zip(self.views[::-1], [x.shape for x in self.views[::-1][1:]]+[out_shape]):
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if (inverted:= v.invert(s)) is None: return None
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inverted_views.append(inverted)
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return ShapeTracker(tuple(inverted_views)).reshape(out_shape)
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@staticmethod
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def from_shape(shape:tuple[sint, ...], strides:tuple[sint, ...]|None=None) -> ShapeTracker: return ShapeTracker((View.create(shape, strides),))
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@property
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def contiguous(self) -> bool: return len(self.views) == 1 and self.views[0].contiguous
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@property
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def consecutive(self) -> bool: return len(self.views) == 1 and (v:=self.views[0]).mask is None and v.strides == strides_for_shape(v.shape)
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@property
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def shape(self) -> tuple[sint, ...]: return self.views[-1].shape
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@property
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def size(self) -> int: return self.views[-1].size()
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def reduce(self, axis:tuple[int, ...]) -> tuple[sint, ...]: return tuple(1 if i in axis else s for i,s in enumerate(self.shape))
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def to_uop(self) -> UOp: return UOp(Ops.VIEW, dtypes.void, (), self)
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def to_indexed_uops(self, _idxs:Optional[list[UOp]|tuple[UOp, ...]]=None) -> tuple[UOp, UOp]:
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return views_to_indexed_uops(self.views, tuple(_idxs) if _idxs is not None else None)
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# upper bound on buffer size required to fit this shapetracker
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def real_size(self) -> int:
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if 0 in self.shape: return 0
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view = (v.shrink(v.mask) if (v:=self.views[0]).mask else v)
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idx, _ = views_to_indexed_uops((view,))
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assert idx.vmax < 1e12, f"real_size broken for {self}"
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return int(idx.vmax + 1)
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def vars(self) -> set[Variable]: return set().union(*[v.vars() for v in self.views])
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@property
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def var_vals(self) -> dict[Variable, int]: return merge_dicts([dict([v.unbind()]) for v in self.vars()])
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def unbind(self) -> tuple[ShapeTracker, dict[Variable, int]]:
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unbound_views, var_vals = zip(*[v.unbind() for v in self.views])
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if all(len(x) == 0 for x in var_vals): return self, {}
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return ShapeTracker(tuple(unbound_views)), merge_dicts(var_vals)
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def real_strides(self, ignore_valid=False) -> tuple[Optional[sint], ...]:
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with Context(TRACK_MATCH_STATS=0): return views_to_real_strides(self.views, ignore_valid)
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def unit_stride_axes(self, ignore_valid=False) -> list[int]: return [i for i,st in enumerate(self.real_strides(ignore_valid)) if st == 1]
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def axis_is_masked(self, axis:int) -> bool:
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with Context(TRACK_MATCH_STATS=0):
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_, valid = self.to_indexed_uops()
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return axis in [x.arg for x in graph_rewrite(valid, symbolic_flat).toposort() if x.op is Ops.RANGE]
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def simplify(self) -> ShapeTracker:
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if len(self.views) >= 2 and (new_view := self.views[-2] + self.views[-1]) is not None:
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return ShapeTracker(self.views[:-2] + (new_view,)).simplify()
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return self
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# *** under this line are the movement ops ***
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def pad(self, arg: tuple[tuple[sint, sint], ...]) -> ShapeTracker: return ShapeTracker(self.views[0:-1] + (self.views[-1].pad(arg), ))
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def shrink(self, arg: tuple[tuple[sint, sint], ...]) -> ShapeTracker: return ShapeTracker(self.views[0:-1] + (self.views[-1].shrink(arg), ))
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def expand(self, new_shape: tuple[sint, ...]) -> ShapeTracker: return ShapeTracker(self.views[0:-1] + (self.views[-1].expand(new_shape), ))
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def permute(self, axis: tuple[int, ...]) -> ShapeTracker: return ShapeTracker(self.views[0:-1] + (self.views[-1].permute(axis), ))
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def flip(self, mul: tuple[int, ...]) -> ShapeTracker: return ShapeTracker(self.views[0:-1] + (self.views[-1].flip(mul), ))
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def reshape(self, new_shape: tuple[sint, ...]) -> ShapeTracker:
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if getenv("MERGE_VIEW", 1) and (new_view := self.views[-1].reshape(new_shape)) is not None: return ShapeTracker(self.views[0:-1] + (new_view,))
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return ShapeTracker(self.views + (View.create(new_shape), ))
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def mop(self, op, arg): return mops[op](self, arg)
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mops: dict[Ops, Callable] = {Ops.RESHAPE: ShapeTracker.reshape, Ops.PERMUTE: ShapeTracker.permute, Ops.EXPAND: ShapeTracker.expand,
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Ops.SHRINK: ShapeTracker.shrink, Ops.FLIP: ShapeTracker.flip, Ops.PAD: ShapeTracker.pad}
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