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96 lines
3.9 KiB
Python
96 lines
3.9 KiB
Python
import heapq
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from typing import Any
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from collections import defaultdict
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from tinygrad.uop.ops import PatternMatcher, UOp, Ops, UPat, multirange_str
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from tinygrad.helpers import prod, getenv, TUPLE_ORDER
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def linearize(sink:UOp) -> list[UOp]:
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# this is a toposort with priority
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lst = list(sink.toposort())
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out_degree:defaultdict[UOp, int] = defaultdict(int)
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priorities:dict[UOp, tuple[int, int, Any]] = {}
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# get consumers and assign priorities
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# NOTE: this requires the lst be locally toposorted
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for u in reversed(lst):
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for s in u.src: out_degree[s] += 1
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# we place UOps with higher run_counts later
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run_count = prod([int(r.vmax)+1 for r in u.ranges])
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# simple priority override. this is all bottom up now, smaller numbers will be closer to the top
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extra = None
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match u.op:
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# the order and placement of these defines is important
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case Ops.PARAM: priority, extra = -20, u.arg
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case Ops.DEFINE_VAR: priority, extra = -19, u.arg
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case Ops.DEFINE_LOCAL: priority = -18
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case Ops.DEFINE_REG: priority = -17
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case Ops.LOAD: priority = -1 # place loads early
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case Ops.STORE: priority = 1 # place stores late
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case Ops.RANGE: priority = 5 # placing RANGE is good
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case Ops.END: priority = -5 # placing END is bad
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case _: priority = 0 # everything else has priority 0
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priorities[u] = (run_count, priority, extra)
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# number the uops in "ideal" order
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nkey = {u:i for i,u in enumerate(sorted(lst, key=lambda x: priorities[x]+(x.tuplize if TUPLE_ORDER else ())))}
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# then force them to be toposorted in as close to the ideal order as possible
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heap = [(-nkey[sink], sink)]
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newlst = []
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while heap:
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newlst.append(u:=heapq.heappop(heap)[1])
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for v in u.src:
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out_degree[v] -= 1
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if out_degree[v] == 0: heapq.heappush(heap, (-nkey[v],v))
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newlst = newlst[::-1]
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if getenv("DEBUG_LINEARIZE"):
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for i,u in enumerate(newlst):
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print(f"{i:4d} {str(u.op):20s} {multirange_str(u.ranges, color=True, pad=10)} {priorities[u]}")
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return newlst
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class CFGContext:
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def __init__(self, sink:UOp):
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# there are 3 relationships between ranges:
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# nested, meaning endrange y is a dependency of endrange x and range x is a dependency of endrange y
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# dependent, meaning endrange y is a dependency of endrange x and range x is not a dependency of endrange y
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# independent, endrange y is not a dependency of endrange x
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# everything is nested inside the sink
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deps: dict[UOp, dict[UOp, None]] = {}
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nesting: dict[UOp, UOp] = {}
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for u in sink.toposort():
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# get the deps from the src
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deps[u] = {}
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for s in u.src: deps[u] |= deps[s]
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if u.op in (Ops.END, Ops.SINK):
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nesting |= {x:u for x in deps[u] if x.op is Ops.END and (u.op is Ops.SINK or u.src[1] in deps[x]) and x not in nesting}
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if u.op in (Ops.RANGE, Ops.END): deps[u][u] = None
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self.edges: dict[UOp, UOp] = {}
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siblings: dict[UOp, list[UOp]] = {}
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for k,vv in nesting.items(): siblings.setdefault(vv, []).append(k)
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for k,v in siblings.items():
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# ranges that have dependencies on other siblings need to be scheduled after them
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order = sorted(v, key=lambda x: len([u for u in v if u in deps[x]]))
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zipped = zip(order, order[1:]) if k.op is Ops.SINK else zip([k.src[1]] + order, order)
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for x,y in zipped:
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# TODO: this can happen! it causes infinite loop in shufflenet
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assert y.src[1] not in x.backward_slice_with_self
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self.edges[y.src[1]] = x
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pm_add_control_flow = PatternMatcher([
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(UPat(Ops.RANGE, name="x"), lambda ctx,x: x.replace(src=x.src+(y,)) if (y:=ctx.edges.get(x)) is not None else None),
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])
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def do_split_ends(e:UOp):
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ret = e.src[0]
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for r in sorted(UOp.sink(*e.src[1:]).ranges, key=lambda x: x.arg, reverse=True): ret = ret.end(r)
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return ret
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pm_split_ends = PatternMatcher([
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# split the ends
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(UPat(Ops.END, name="e"), do_split_ends),
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]) |