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116 lines
5.7 KiB
116 lines
5.7 KiB
import math
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from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType
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from tinygrad.helpers import all_int, partition, flatten, prod, dedup
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from tinygrad.dtype import dtypes
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from tinygrad.shape.view import get_contraction
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from tinygrad.renderer import Renderer
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def _group_dims(dims:tuple[sint, ...], max_sizes:tuple[int, ...]):
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# TODO: symbolic shape
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if not all_int(dims): return dims
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while len(dims) > len(max_sizes) or any(d > m for d,m in zip(dims, max_sizes)):
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for i,m in enumerate(max_sizes):
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if i < (len(dims)-1) and dims[i] * dims[i+1] <= m:
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dims = dims[:i] + (dims[i]*dims[i+1],) + dims[i+2:]
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break
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else: return None
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return dims
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def _split_dims(dims, max_sizes):
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if all(d <= m for d,m in zip(dims, max_sizes)): return dims
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_dims = list(dims) + [1]*(3-len(dims))
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for i in range(len(_dims)):
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while _dims[i] > max_sizes[i]:
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div = next((d for d in range(2, math.ceil(math.sqrt(_dims[i])) + 1) if (_dims[i] % d) == 0), 1)
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if div == 1: raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
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_dims[i], _dims[(i+1)%len(_dims)] = _dims[i]//div, _dims[(i+1)%len(_dims)]*div
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return tuple(_dims[:2] if _dims[2] == 1 else _dims[0] if _dims[1:3] == [1,1] else _dims)
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def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|None, reverse=False) -> list[UOp]:
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if reverse: dims = dims[::-1]
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# try to group first: (a, b, c, d) -> (ab, c, d)
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limited = (grouped if (grouped := _group_dims(dims, max_sizes)) else dims) if max_sizes is not None else dims
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# check if grouping failed
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if max_sizes is not None and len(limited) > len(max_sizes): raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
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# try to split up dims: (a,) -> (b, c)
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if limited == dims: limited = _split_dims(dims, max_sizes) if max_sizes is not None else dims
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ret = raw_idxs = [UOp(Ops.SPECIAL, dtypes.int, (), (f"{prefix}{i}", s)) for i,s in enumerate(limited)]
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if len(limited) < len(dims):
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ret = []
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if (contraction:=get_contraction(dims, limited)) is None: raise AssertionError(f"get_contraction should not be None {dims=} {limited=}")
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for idx, contraction_group in zip(raw_idxs, contraction):
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for c in contraction_group[:-1]:
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ret.append(idx % dims[c])
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idx //= dims[c]
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ret.append(idx)
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elif len(limited) > len(dims):
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a, b = len(limited), len(dims)
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if a == 2 and b == 1: ret = [raw_idxs[0] * limited[1] + raw_idxs[1]]
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if a == 3 and b == 1: ret = [raw_idxs[0] * (limited[1] * limited[2]) + raw_idxs[1] * limited[2] + raw_idxs[2]]
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if a == 3 and b == 2: ret = [raw_idxs[0] * limited[1] + raw_idxs[1], raw_idxs[2]]
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return ret[::-1] if reverse else ret
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def add_gpudims(ctx:Renderer, s:UOp):
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if s.arg is None: return None
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s_topo = list(s.toposort())
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if any(x.op is Ops.SPECIAL for x in s_topo): return None
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# get ranges
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all_ranges = {x.arg[0]%1000:x for x in s_topo if x.op is Ops.RANGE}
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# extract global/local dims
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global_dims = sorted(dedup([x.arg[0]%1000 for x in all_ranges.values() if x.arg[1] is AxisType.GLOBAL]))
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local_dims = sorted(dedup([x.arg[0]%1000 for x in all_ranges.values() if x.arg[1] in (AxisType.LOCAL, AxisType.GROUP_REDUCE)]))
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if not global_dims and not local_dims: return None
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# get global and local shape
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ranges = [all_ranges[r] for r in global_dims+local_dims if r in all_ranges]
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global_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg[0]%1000 in global_dims])
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local_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg[0]%1000 in local_dims])
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# get the idxs
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ki: KernelInfo = s.arg
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if ki.dont_use_locals:
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assert not local_dims, "can't use locals if there's no local dims"
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idxs = get_grouped_dims("idx", global_shape, ctx.global_max, reverse=True)
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else:
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# define indexes for GPU-like execution
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idxs = get_grouped_dims("gidx", global_shape, ctx.global_max, reverse=True) + get_grouped_dims("lidx", local_shape, ctx.local_max)
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# apply to multiple ranges
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subs = {}
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for r in s_topo:
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if r.op is not Ops.RANGE: continue
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try:
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ii = (global_dims+local_dims).index(r.arg[0]%1000)
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if r.arg[0] < 2000 and r.arg[1] == AxisType.GROUP_REDUCE: continue
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subs[r] = idxs[ii]
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except ValueError: continue
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return s.substitute(subs)
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def fix_reduce_unroll(x:UOp):
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reduce_range, reduce_expand = partition(x.src[1:], lambda y: y.op is Ops.RANGE)
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if len(reduce_expand) == 0: return None
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reduce_expand = [x for x in reduce_expand if x.op is not Ops.CONST]
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assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand}"
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ret = x.src[0]
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if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
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ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis), tag=1)
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# REDUCE supports both "horizontal" reduction and range reduction. the horizontal elements are taken in the nearest group
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return x.replace(src=(ret,)+tuple(reduce_range))
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def fix_store_unroll(x:UOp):
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store_expand, store_range = partition(x.src[2:], lambda y: y.op is Ops.UNROLL)
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if len(store_expand) == 0: return None
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return UOp(Ops.CONTRACT, dtypes.void, (x.replace(src=x.src[:2]+tuple(store_range)),), tuple(flatten(x.arg for x in store_expand)), tag=1)
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pm_add_gpudims = PatternMatcher([
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(UPat(Ops.SINK, name="s"), add_gpudims),
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# rewrite UPCAST/UNROLL range to something to be expanded
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(UPat(Ops.RANGE, name="r"),
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lambda r: UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s:=r.vmax+1), tuple(range(s))),), ((r.arg[0],s),)) \
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if r.arg[1] in {AxisType.UNROLL, AxisType.UPCAST} else None),
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# fix REDUCEs with UNROLLs
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(UPat(Ops.REDUCE, name="x"), fix_reduce_unroll),
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(UPat(Ops.STORE, name="x"), fix_store_unroll),
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])
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