from dataclasses import dataclass, field import itertools from tinygrad.dtype import dtypes, PtrDType, ImageDType, AddrSpace from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, _substitute, ssimplify, KernelInfo from tinygrad.uop.ops import track_rewrites, graph_rewrite, identity_element, sint, AxisType, BottomUpGate, Kernel, _remove_all_tags from tinygrad.uop.symbolic import symbolic from tinygrad.helpers import argsort, prod, all_same, pluralize, getenv, flatten, dedup, all_int, DEBUG, SPLIT_REDUCEOP, DEBUG_RANGEIFY from tinygrad.helpers import PCONTIG, partition, get_single_element, unwrap, disable_gc from tinygrad.codegen.simplify import pm_flatten_range, pm_reduce_simplify from tinygrad.codegen.opt import Opt from tinygrad.schedule.indexing import run_rangeify, BufferizeOpts, ALWAYS_CONTIGUOUS, IndexingContext, apply_movement_op # creation can recurse a lot import sys sys.setrecursionlimit(10000) # movement op on INDEX as a PatternMatcher pm_mops = PatternMatcher([ (UPat(GroupOp.Movement, name="r").f(Ops.INDEX, allow_any_len=True, name="idx"), lambda r,idx: r.src[0].index(*apply_movement_op(r.op, r.src[0].shape, r.marg, idx.src[1:]), dtype=idx.dtype, arg=idx.arg)), # move movement ops after AFTER (UPat(GroupOp.Movement, name="r").after(name="a", allow_any_len=True), lambda r,a: UOp(r.op, r.dtype, (a.replace(src=(r.src[0],)+a.src[1:], tag=None),)+r.src[1:], r.arg, tag=a.tag)), (UPat(GroupOp.Movement, name="r").end(name="a", allow_any_len=True), lambda r,a: a.replace(src=(r.src[0],)+a.src[1:])), ]) # ***************** # 0. do some cleanup rewrites, mostly copied from the old stuff def find_permutes(a:UOp, b:UOp, assign:UOp): if not (permutes:=[s for s in b.toposort(gate=lambda s:s.op not in ALWAYS_CONTIGUOUS) if s.op in GroupOp.Movement and s.op not in {Ops.RESHAPE, Ops.EXPAND, Ops.PAD, Ops.SHRINK}]): return target = a.base for p in permutes: if any(s is target for s in p.toposort(gate=lambda s:s.op not in ALWAYS_CONTIGUOUS-{Ops.BUFFER})): return assign.replace(src=(a, b.contiguous())) def split_reduceop(reduce:UOp, x:UOp): if prod(reduce.shape) == 0: return None if not SPLIT_REDUCEOP or not all_int(x.shape) or (prod(x.shape)//prod(reduce.shape))1) else UOp.const(dtypes.index, 0) for i,s in enumerate(x.shape)]) range_nums = [y.arg[0] for y in indexed.substitute({x.base:UOp(Ops.NOOP)}, extra_pm=pm_mops).ranges] is_expanded = [i not in range_nums for i in range(len(x.shape))] if not (split_candidates:=[(i,d) for i in reduce.arg[1] for d in range(min(256,2**getenv("REDUCEOP_SPLIT_SIZE",22)//prod(reduce.shape)),8-1,-1) if x.shape[i]%d==0 and not is_expanded[i]]): return None dim_to_split, divisor = split_candidates[0] splitted_shape = x.shape[:dim_to_split]+(divisor,)+(x.shape[dim_to_split]//divisor,)+x.shape[dim_to_split+1:] splitted = x.reshape(splitted_shape).permute(tuple([d for d in range(len(splitted_shape)) if d!=dim_to_split]+[dim_to_split])) if DEBUG >= 3: print(f"split {divisor}: {x.shape} -> {splitted.shape} -> {reduce.shape}") # reduce original axes, then split return splitted.r(*reduce.arg).contiguous().r(reduce.arg[0], (len(reduce.shape),)).reshape(reduce.shape).replace(tag=reduce.tag) mop_cleanup = PatternMatcher([ # merge adjacent RESHAPES, safe because they are not tagged (UPat(Ops.RESHAPE, name="x2").f(Ops.RESHAPE, allow_any_len=True, name="x"), lambda x,x2: x.replace(src=(x2.src[0], x.src[1])) if x.tag is None and x2.tag is None else None), ]) earliest_rewrites = mop_cleanup+PatternMatcher([ # just removing it works... (UPat((Ops.DETACH, Ops.CONTIGUOUS_BACKWARD), name="x"), lambda x: x.src[0]), # remove CONTIGUOUS if the BUFFER is already contiguous (UPat(Ops.BUFFER).f(Ops.RESHAPE, allow_any_len=True, name="r").f(Ops.CONTIGUOUS, name="c"), lambda r,c: r.replace(tag=c.tag)), # split_reduceop (UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)), split_reduceop), # preserve tags? # reduce of size 0 is the identity element (UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)), lambda reduce,x: reduce.const_like(identity_element(reduce.arg[0], reduce.dtype)) if x.size == 0 and reduce.size != 0 else None), # handle size 0 (UPat(GroupOp.All-{Ops.SINK}, name="x"), lambda x: x.const_like(0).rtag(x.tag) if x._shape is not None and x.size == 0 else None), # remove contiguous on movement ops before a copy on disk (UPat(GroupOp.Movement-{Ops.SHRINK, Ops.RESHAPE}, name="x").f(Ops.CONTIGUOUS).f(Ops.COPY, allow_any_len=True, name="copy"), lambda x,copy: copy.replace(src=(x,)+copy.src[1:]) if isinstance(x.device, str) and x.device.startswith("DISK") else None), # push copy past movement ops to disk (UPat(GroupOp.Movement-{Ops.SHRINK, Ops.RESHAPE}, name="x").f(Ops.COPY, allow_any_len=True, name="copy"), lambda x,copy: x.replace(src=(copy.replace(src=(x.src[0],)+copy.src[1:], tag=None),)+x.src[1:], tag=copy.tag) \ if isinstance(x.device, str) and x.device.startswith("DISK") else None), # ** copy rules ** # early fixup const copy (UPat(Ops.COPY, src=(UPat.var("s"), UPat()), name="c"), lambda c,s: c.const_like(ss.arg) if (ss:=s.base).op is Ops.CONST else None), # COPY and source size need to match # TODO: expand after copy creates issues with tagging (UPat(Ops.COPY, src=(UPat(GroupOp.Movement, name="r"), UPat(name="d")), name="c"), lambda c,r,d: c.replace(src=(r.contiguous(), d)) if r.size != r.base.size else None), # copy only to different device (UPat(Ops.COPY, src=(UPat.var("x"), UPat()), name="copy"), lambda x,copy: x.f(Ops.NOOP, tag=copy.tag) if x.device == copy.device else None), # ** assign rules ** # assign only to buffer, otherwise make it a CONTIGUOUS (UPat(Ops.ASSIGN, src=(UPat(GroupOp.All-{Ops.BUFFER}, name="target"), UPat(name="x")), name="assign"), lambda x,target,assign: x.f(Ops.CONTIGUOUS, tag=assign.tag) if ((t:=target.base).op is not Ops.BUFFER and \ not (t.op is Ops.MSTACK and all(s.op is Ops.BUFFER for s in t.src))) else None), # realize before assign if input permutes the target buffer (UPat(Ops.ASSIGN, src=(UPat.var("a"), UPat.var("b")), name="assign"), find_permutes), ]) # ***************** # 3.5 cleanups # Ops.NOOP happens when we have a COPY to the device the Tensor is already on. We treat it like COPY here for MSTACK. ALWAYS_RUN_OPS = {Ops.CONTIGUOUS, Ops.COPY, Ops.ASSIGN, Ops.NOOP} # you don't know in the first pass if axes are going to die, this happens if there's an EXPAND to the left def cleanup_dead_axes(b:UOp): # don't optimize ALWAYS_RUN_OPS if b.src[0].op in ALWAYS_RUN_OPS: return None new_rng = [] hit = False reshape: list[sint] = [] for s,rng in zip(b.shape, b.src[1:]): # skip for symbolic. TODO: fix this if rng.op is Ops.RANGE and rng.src[0].op is not Ops.CONST: return None # CONSTs are already dead axes if rng.op is Ops.CONST or (rng.op is Ops.RANGE and rng not in b.src[0].ranges): reshape.append(1) hit = True else: reshape.append(s) new_rng.append(rng) if hit: # move the tag to the expand. NOTE: this expand tag might not survive return b.replace(src=b.src[0:1]+tuple(new_rng), tag=None).reshape(tuple(reshape)).expand(b.shape).replace(tag=b.tag) def gate_substitute(ctx, b:UOp) -> None: if not any(r in b.ranges for r in ctx.keys()): raise BottomUpGate() pm_gate_substitute = PatternMatcher([(UPat(GroupOp.All, name="b"), gate_substitute)], compiled=False) # if a buffer is being stored just for permutes or something, remove it # we want to reexpress the indexes of idx2 in terms of the implied b1 def remove_bufferize(src:UOp, buf:UOp, idx:UOp): # see if we can't do it, should this ever hit? assert len(buf.src) == len(idx.src), f"index on wrong bufferize, {len(buf.src)} != {len(idx.src)}" assert all(x.op in {Ops.RANGE, Ops.CONST} for x in buf.src[1:]) # if it's user contiguous, we never remove it if src.op in ALWAYS_RUN_OPS: return None # we don't want to bufferize threefry, also causes problems because not all platforms support long if src.op is not Ops.THREEFRY: # *** here is where we compute the cost *** # if we return None, the bufferize is kept accessed_buffers: list[UOp] = [] indexes: list[UOp] = [] reduces: list[UOp] = [] def red_gate(x:UOp): if x.op is Ops.BUFFERIZE and x.arg.addrspace == AddrSpace.GLOBAL: accessed_buffers.append(x) return False if x.op is Ops.BUFFER: accessed_buffers.append(x) if x.op is Ops.INDEX: indexes.append(x) if x.op is Ops.REDUCE: reduces.append(x) return True src.toposort(gate=red_gate) del red_gate accessed_buffers = dedup(accessed_buffers) # if this is generated from multiple buffers, don't remove this buffer if len(accessed_buffers) > 2 and not (PCONTIG > 2): return None # if any reduces access a buffer, don't remove this buffer buffer_in_reduce = False def buf_gate(x:UOp): nonlocal buffer_in_reduce if x.op in {Ops.BUFFER, Ops.BUFFERIZE}: buffer_in_reduce = True return not buffer_in_reduce UOp.sink(*[x.src[0] for x in reduces]).toposort(gate=buf_gate) del buf_gate if buffer_in_reduce: if PCONTIG > 2: out_in_ratio = (prod(buf.shape)+1) / (sum([x.size for x in accessed_buffers])+1) if out_in_ratio < 10: return None # here we have to check the indexes, we might do a partial contig here local_indexes = [x for x in indexes if x.src[0].op is Ops.BUFFERIZE and x.src[0].arg.addrspace == AddrSpace.LOCAL] exclude_ranges = UOp.group(*[UOp.group(*x.src[1:]) for x in local_indexes]).ranges subs = [(k,v) for k,v in zip(buf.src[1:], idx.src[1:]) if k.op is not Ops.CONST] # if it's bufferized or a reduce, it's pcontig is_pcontig, is_subs = partition(subs, lambda x: x[0] in exclude_ranges or any([r.arg[-1] == AxisType.REDUCE for r in x[1].ranges])) if not len(is_subs): return None if len(is_pcontig): ret = src.substitute(dict(is_subs), extra_pm=pm_gate_substitute) return ret.bufferize(*[x[0] for x in is_pcontig], arg=BufferizeOpts(None, AddrSpace.LOCAL)).index(*[x[1] for x in is_pcontig]) else: return None # if it makes it here, the bufferize is removed # this is the ranges replaced # NOTE: if buf src is a const, we don't replace it return src.substitute({k:v for k,v in zip(buf.src[1:], idx.src[1:]) if k.op is not Ops.CONST}, extra_pm=pm_gate_substitute) def remove_noop_bufferize(idx,b2): if idx.src[1:] != b2.src[1:] or idx.src[0].op is Ops.BUFFER_VIEW: return None new_tag = (idx.src[0].tag or ()) + (b2.tag or ()) or None return idx.src[0].rtag(new_tag).shrink(tuple((0, s) for s in b2.shape)) if b2.shape else idx.src[0].rtag(new_tag) pm_const_buffer_folding = pm_mops+PatternMatcher([ (UPat(Ops.BUFFERIZE, name="b"), cleanup_dead_axes), (UPat(GroupOp.All-{Ops.BUFFERIZE, Ops.BUFFER}, name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None), (UPat((Ops.BUFFERIZE), name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) and (resolve(prod(x.dtype.shape)!=prod(x.shape)) or x.shape[-1]%4!=0) else None), # remove noop buffers. if we look at the next index we can remove even more of these (UPat(Ops.INDEX, name="idx").f(Ops.BUFFERIZE, allow_any_len=True, name="b2"), remove_noop_bufferize), # dont bufferize an arange (UPat.any((r:=UPat(dtype=dtypes.index).cast()).named("src"), r.eq(UPat()).named("src")).f(Ops.BUFFERIZE, allow_any_len=True, name="buf").f(Ops.INDEX, allow_any_len=True, name="idx"), remove_bufferize), # no buffers for const (UPat(Ops.CONST, name='c').f(Ops.BUFFERIZE, allow_any_len=True, name="b"), lambda c,b: b.const_like(c.arg).rtag(b.tag)), # indexing a const is a const (UPat(Ops.INDEX, src=(UPat(Ops.CONST, name="c"),),), lambda c: c), # copy on CONST is CONST (UPat(Ops.COPY, src=(UPat.cvar("x"), UPat()), name="copy"), lambda copy,x: copy.const_like(x.arg)), # hack if a noop turned to a const (UPat.cvar("c").f(Ops.NOOP).f(Ops.BUFFERIZE, allow_any_len=True, name="buf"), lambda c,buf: buf.replace(src=(c,)+buf.src[1:])), # mstack on CONST is CONST (UPat(Ops.MSTACK, src=(UPat.var("s"),), allow_any_len=True).f(Ops.INDEX, allow_any_len=True), lambda s: UOp.const(c.dtype, c.arg) if (c:=s.base).op is Ops.CONST else None), ]) def pre_bufferize(b:UOp, x:UOp, copy:UOp): nb = b.replace(src=(b.src[0].contiguous(),)+b.src[1:]) return copy.replace(src=(x.replace(src=(nb,)+x.src[1:]), copy.src[1])) pm_remove_bufferize = PatternMatcher([ # hack so remove_bufferize doesnt remove the buffer before a copy (UPat(Ops.COPY, src=(UPat(GroupOp.All-{Ops.CONTIGUOUS, Ops.COPY}).f(Ops.BUFFERIZE, allow_any_len=True, name="b") .f(Ops.INDEX, allow_any_len=True, name="x"), UPat()), name="copy"), pre_bufferize), # remove reindexing with cost function (UPat.var("src").f(Ops.BUFFERIZE, allow_any_len=True, name="buf").f(Ops.INDEX, allow_any_len=True, name="idx"), remove_bufferize), ]) def late_buffer_view(t:UOp, b:UOp): if isinstance(b.device, str) and (b.device.startswith("DISK") or b.device.startswith("TINYFS")): shape = b.shape size = prod(shape) # walk up for the INDEX x = t while not any(u.op is Ops.INDEX for u in x.src): assert x.op not in GroupOp.Elementwise, "can't buffer view elementwise" x = x.src[0] x = next(u for u in x.src if u.op is Ops.INDEX) if len(shape) == 0: offset = x.src[1].arg else: offset = max(sum(idx.vmin for idx in x.src[1:]), 0) return b.replace(src=(UOp(Ops.BUFFER_VIEW, t.dtype, (x.base,), (size, offset), tag=t.tag),) + b.src[1:]) return b to_bufferview = PatternMatcher([ (UPat((Ops.BITCAST, Ops.CONTIGUOUS), name="t").f(Ops.BUFFERIZE, allow_any_len=True, name="b"), late_buffer_view), (UPat((Ops.BITCAST, Ops.CONTIGUOUS)).f(Ops.BUFFER_VIEW, name="b"), lambda b: b.replace(src=b.src[0].src)), ]) DEVICE_MAX_BUFS = {"METAL": 31, "WEBGPU": 8} # TODO: get from device? def limit_bufs(ctx:IndexingContext, root:UOp): if (device:=root._device) is None: return None # no device, index related calculations device = device if isinstance(device, str) else device[0].split(":")[0] if not (MAX_BUFS:=getenv("MAX_KERNEL_BUFFERS", DEVICE_MAX_BUFS.get(device, 0))): return None bufs: set[UOp] = set() def gate_input(u:UOp): # TODO: add cache to fix n^2 if is_load:=(u.op in {Ops.BUFFERIZE, Ops.AFTER, Ops.BUFFER, Ops.MSELECT, Ops.MSTACK, Ops.DEFINE_VAR}): bufs.add(u) return not is_load root.toposort(gate=gate_input) if len(bufs) > MAX_BUFS - 1: # NOTE: this -1 is for the output buffer srcs = [] for s in root.src: if s.op in GroupOp.Elementwise: # Insert bufferize: all AxisType.REDUCE before bufferize are AxisType.LOOP orig_ranges, end_ranges = s.ranges, [x.replace(arg=(next(ctx.range_idx), AxisType.LOOP)) if x.op is Ops.RANGE else x for x in s.ranges] s = s.substitute(dict(zip(orig_ranges, end_ranges))).bufferize(*end_ranges, arg=BufferizeOpts(device=s.device)).index(*orig_ranges) srcs.append(s) return root.replace(src=tuple(srcs)) pm_limit_bufs = PatternMatcher([(UPat(set.union(GroupOp.Binary, GroupOp.Ternary), name="root"), limit_bufs)]) # ***************** # 4. put in buffers for bufferize # TODO: should BUFFERIZE look a lot more like STORE # BUFFERIZE has device in arg # BUFFERIZE doesn't have indexing, that's implied by the ranges it closes # BUFFERIZE returns the BUFFER ready for INDEXing (doing this will make splitting a lot easier) # NOTE: this has been fixed up a bit def bufferize_to_store(ctx:itertools.count|None, x:UOp, idx:UOp, allow_locals=True): #assert isinstance(x.tag, Flat), "bufferize must be flat" size = prod(x.shape) rngs = sorted(idx.ranges, key=lambda x: x.arg) assert size > 0 and isinstance(size, int), f"no zero sized or symbolic sized buffers {size}" sdtype = x.dtype.ptr(size=size, addrspace=x.arg.addrspace) if x.src[0].op is Ops.ASSIGN: assign_target, assign_src, assign_mops = x.src[0].src assert assign_target.op is Ops.INDEX, f"{assign_target.op} is not index" # in assign, this is the buffer size, not the bufferize size # TODO: assign_mops here do_store = assign_target.replace(dtype=sdtype).store(assign_src, tag=x.tag).end(*rngs) ret = assign_target.src[0].after(do_store) mops = [] walk = assign_mops while walk is not assign_mops.base: mops.append((walk.op, walk.marg)) walk = walk.src[0] for m in mops[::-1]: ret = ret._mop(*m) return ret # NOTE: the DEFINE_LOCAL needs to be disambiguated here if sdtype.addrspace == AddrSpace.GLOBAL: buf = UOp.new_buffer(x.arg.device, size, x.dtype) do_store = buf.index(idx, dtype=sdtype).store(x.src[0], tag=x.tag).end(*rngs) return buf.after(do_store) if allow_locals: # handle locals buf = UOp(Ops.DEFINE_LOCAL, sdtype, arg=next(unwrap(ctx))) do_store = buf.broadcast(x.src[1].dtype.count).index(idx, dtype=sdtype).store(x.src[0]).end(*rngs) return buf.after(do_store.barrier()) # collapse any BUFFERIZE to single input BUFFERIZE. move the tag to a reshape def flatten_bufferize(x:UOp): if x.tag is None and len(x.src) == 2: return None ret = x.replace(tag=None, src=(x.src[0], get_single_element(apply_movement_op(Ops.RESHAPE, (prod(x.shape),), x.shape, x.src[1:])))) rngs = x.src[1:] ret = ret.forced_reshape(x.shape) if any(r.op is Ops.RANGE and r.src[0].op is not Ops.CONST for r in rngs): sym_shape = tuple([ssimplify(r.src[0]) if r.op is not Ops.CONST else 1 for r in rngs]) ret = ret.shrink(tuple([(0,x) for x in sym_shape])) return ret.rtag(x.tag) pm_flatten_bufferize = PatternMatcher([(UPat(Ops.BUFFERIZE, name="x"), flatten_bufferize)]) pm_add_buffers = pm_mops+pm_flatten_bufferize+to_bufferview+PatternMatcher([ (UPat(Ops.BUFFERIZE, src=(UPat(), UPat(name="idx")), name="x"), lambda x, idx: bufferize_to_store(None, x, idx, allow_locals=False)), # move RESHAPEs through MSELECT/MSTACK (UPat((Ops.MSELECT, Ops.MSTACK), src=UPat(Ops.RESHAPE), name="m"), lambda m: m.replace(src=tuple([x.src[0].base for x in m.src]), tag=None).reshape(m.shape).rtag(m.tag)), # remove any RESHAPEs on KERNEL (UPat(Ops.KERNEL, name="k"), lambda k: k.replace(src=tuple(x.src[0] if x.op is Ops.RESHAPE else x for x in k.src))), ]) pm_add_buffers_local = pm_mops+pm_flatten_bufferize+to_bufferview+PatternMatcher([ (UPat(Ops.BUFFERIZE, src=(UPat(), UPat(name="idx")), name="x"), bufferize_to_store), ]) # ***************** # 5. split into kernels @dataclass class LocalAddBufferContext: dg:int = 0 map:dict = field(default_factory=dict) vars:dict = field(default_factory=dict) range:int = 0 parent_tags:list = field(default_factory=list) opts:tuple|None = None def debuf(ctx:LocalAddBufferContext, buf:UOp): ret = UOp(Ops.DEFINE_GLOBAL, buf.dtype.ptr(buf.arg), arg=ctx.dg) if buf not in ctx.map: ctx.map[buf] = buf ctx.dg += 1 return ret def unbind_kernel(ctx:LocalAddBufferContext, b:UOp): ctx.vars[b] = None return b.src[0] def handle_after(ctx:LocalAddBufferContext, after:UOp): if isinstance(after.dtype, PtrDType) and after.ptrdtype.addrspace == AddrSpace.LOCAL: return None buf = after.as_buf() # HACK to put the buffer in the MAP instead of MSTACK/MSELECT if buf.op in {Ops.MSTACK, Ops.MSELECT}: buf = buf.src[0] assert buf not in ctx.map ctx.map[buf] = after return buf def renumber_range(ctx:LocalAddBufferContext, r:UOp): if r.tag != (): return None ret = r.replace(arg=(ctx.range,)+r.arg[1:], tag=None) ctx.range += 1 return ret def find_bufs(x:UOp): idxs = [s for s in x.toposort(gate=lambda x: x.op is not Ops.AFTER) if s.op is Ops.INDEX] read_from: dict[UOp, Ops] = {} if any((buf:=idx.as_buf()).op is Ops.BUFFER and read_from.setdefault(buf, op:=idx.src[0].op) is not op for idx in idxs): raise RuntimeError(f"cycle detected while indexing {buf}") to_define_global = PatternMatcher([ (UPat(Ops.STORE, name="x"), find_bufs), (UPat(Ops.BUFFER, name="buf"), debuf), (UPat(Ops.BIND, name="b"), unbind_kernel), (UPat((Ops.MSTACK, Ops.MSELECT, Ops.AFTER), name="after"), handle_after), # HACK in case any CONSTs were replaced # this is only needed if you are using symbolic (UPat((Ops.CONST, Ops.DEFINE_VAR), name="c"), lambda c: c.replace(src=()) if len(c.src) else None), # remove RANGE with 0 size (UPat(Ops.RANGE, name="r"), lambda r: UOp.const(dtypes.index, 0) if r.vmax == 0 else None), # renumber the ranges starting with 0 so that kernel deduping works (UPat(Ops.RANGE, name="r"), renumber_range), ]) def get_contiguous(ctx:LocalAddBufferContext, x:UOp): if isinstance(x.arg, tuple) and all(isinstance(y, Opt) for y in x.arg): ctx.opts = x.arg return x.src[0] rangeify_codegen = PatternMatcher([ (UPat(Ops.CONTIGUOUS, name="x"), get_contiguous), # no NOOP in the kernel graph # TODO: this can be moved into codegen? (UPat(Ops.NOOP, name="x"), lambda x: x.src[0]), # strip the arg from store (UPat(Ops.STORE, name="x"), lambda x: x.replace(arg=None) if x.arg is not None else None), # add loads to non ptr indexes # TODO: this can be moved into codegen? #(UPat.any(UPat(Ops.DEFINE_GLOBAL, name="dg"), UPat(Ops.DEFINE_LOCAL).f(Ops.AFTER, allow_any_len=True, name="dg")) # .f(Ops.INDEX, name="idx", allow_any_len=True), # lambda dg,idx: None if isinstance(idx.dtype, (PtrDType, ImageDType)) else idx.replace(dtype=dg.dtype, arg=None).load()), # fix broadcast dtype (UPat(Ops.AFTER, name="a").broadcast(name="b"), lambda a,b: a.broadcast(len(b.src))), (UPat(Ops.DEFINE_LOCAL).f(Ops.AFTER, allow_any_len=True).broadcast(name="dg").f(Ops.INDEX, name="idx", allow_any_len=True), lambda dg,idx: None if isinstance(idx.dtype, (PtrDType, ImageDType)) else idx.replace(dtype=dg.dtype, arg=None).load(dtype=dg.dtype.base.scalar().vec(dg.dtype.vcount))), (UPat(Ops.AFTER, name="a").gep(name="b"), lambda a,b: a.gep(b.arg)), (UPat(Ops.DEFINE_LOCAL).f(Ops.AFTER, allow_any_len=True).gep(name="dg").f(Ops.INDEX, name="idx", allow_any_len=True), lambda dg,idx: None if isinstance(idx.dtype, (PtrDType, ImageDType)) else idx.replace(dtype=dg.dtype, arg=None).load(dtype=dg.dtype.base.scalar().vec(dg.dtype.vcount))), ]) def remove_metadata_tags(ctx:LocalAddBufferContext, x:UOp): if x.tag is None or x.tag == (): return None if isinstance(x.tag, tuple): ctx.parent_tags += list(x.tag) return x.replace(tag=None) pm_remove_tags = PatternMatcher([ (UPat(GroupOp.All, name="x"), remove_metadata_tags), ]) pm_add_range_tags = PatternMatcher([ (UPat(Ops.RANGE, name="x"), lambda x: x.rtag(())), ]) def split_store(ctx:list[UOp], x:UOp) -> UOp|None: if len(x.ranges): return None # local kernel rewrite lctx = LocalAddBufferContext() ret = graph_rewrite(x, to_define_global+pm_flatten_range+rangeify_codegen+pm_remove_tags, ctx=lctx, name="kernel split", bottom_up=True) # gather the metadata metadatas = [ctx[y].metadata for y in lctx.parent_tags] # NOTE: the hack for COPY is here for u in ret.toposort(): # TODO: this can be wrong if there's multiple of these if u.op in {Ops.COPY, Ops.BUFFER_VIEW}: ret = u break else: ret = ret.sink(arg=KernelInfo(opts_to_apply=lctx.opts) if lctx.opts is not None else None) kernel_arg = Kernel(ret,tuple(dedup(flatten([x for x in metadatas if x is not None])))[::-1]) kernel = UOp(Ops.KERNEL, src=tuple(lctx.map.values())+tuple(lctx.vars.keys()), arg=kernel_arg) if ret.op is Ops.SINK and not all_same([x.device for x in kernel.src if x.op is not Ops.BIND]): raise RuntimeError(f"all buffers must be on the same device: {tuple(b.buf_uop.buffer for b in kernel.src)}") return kernel split_kernels = PatternMatcher([ (UPat((Ops.STORE, Ops.END), name="x"), split_store), ]) def tag_uop(ctx:list[UOp], x:UOp): if x.tag is not None: return None if x.dtype.scalar() == dtypes.index: return None ctx.append(x) return x.replace(tag=(len(ctx)-1,)) add_tags = PatternMatcher([ # don't tag BUFFERs, they are global (UPat(GroupOp.All-{Ops.BUFFER, Ops.CONST, Ops.DEVICE, Ops.UNIQUE, Ops.DEFINE_VAR, Ops.BIND, Ops.KERNEL, Ops.MSTACK, Ops.MSELECT, Ops.RANGE}.union(GroupOp.Movement), name="x"), tag_uop), (UPat({Ops.MSTACK, Ops.MSELECT}, name="x"), lambda ctx,x: None if all(s.op is Ops.BUFFER for s in x.src) else tag_uop(ctx, x)), ]) # support for using a contiguous permuted view instead of the parent view if one exists # modified from kernelize.py to not use ShapeTracker def found_contiguous(ctx:dict[UOp, UOp], contig:UOp, src:UOp): x = src while x is not src.base: if x.op is Ops.PERMUTE: contig = contig.permute(argsort(x.marg)) elif x.op is Ops.RESHAPE: contig = contig.reshape(x.src[0].shape) else: return None x = x.src[0] ctx[src.base] = contig replace_contiguous = PatternMatcher([ (UPat(Ops.CONTIGUOUS, src=(UPat(GroupOp.Movement, name="src"),), name="contig"), found_contiguous), (UPat(GroupOp.ALU, name="alu"), lambda ctx,alu: alu.replace(src=new_src) if (new_src:=tuple(ctx.get(s, s) for s in alu.src)) != alu.src else None), ]) @disable_gc() @track_rewrites(lambda _,ret: f"Schedule {pluralize('Kernel', len([u for u in UOp.sink(*ret.values()).toposort() if u.op is Ops.KERNEL]))}", True) def get_rangeify_map(sink:UOp) -> dict[UOp, UOp]: if getenv("VIZ"): graph_rewrite(sink, PatternMatcher([]), name="View Input Graph") uop_list: list[UOp] = [] tsink = graph_rewrite(sink, add_tags, ctx=uop_list, bottom_up=True, name="number the uops") tsink = graph_rewrite(tsink, pm_mops+earliest_rewrites+replace_contiguous, ctx={}, name="earliest rewrites") # convert movement ops to ranges tsink, rctx = run_rangeify(tsink, DEBUG_RANGEIFY) tsink = graph_rewrite(tsink, symbolic+pm_reduce_simplify+pm_const_buffer_folding, name="symbolic+reduce_collapse") # this does const folding tsink = graph_rewrite(tsink, pm_remove_bufferize, bottom_up=True, name="remove bufferize with cost function") tsink = graph_rewrite(tsink, symbolic+pm_reduce_simplify+pm_const_buffer_folding, name="symbolic+reduce_collapse pt 2") tsink = graph_rewrite(tsink, pm_limit_bufs, ctx=rctx, name="limit buffers") # rebuild the sink with all the BUFFERIZEs with tags, this is what's ending up in the tensor graph # MSTACK stacks multiple BUFFERIZEs in one tagged tensor # if it's not tagged by here, it's out tsink = UOp.sink(*[x for x in tsink.backward_slice if x.base.op in {Ops.BUFFERIZE, Ops.MSTACK, Ops.CONST, Ops.BUFFER, Ops.AFTER} and \ x.tag is not None and len(x.tag)]) if getenv("VIZ"): graph_rewrite(tsink, PatternMatcher([]), name="View Tagged Rangeify") # bufferize -> store tsink = graph_rewrite(tsink, pm_add_buffers+pm_add_range_tags, bottom_up=True, name="bufferize to store") tsink = graph_rewrite(tsink, split_kernels, ctx=uop_list, name="split kernels") # if a kernel depends on a buffer, and that buffer is later assigned to, make the assign depend on the kernel's assign kernel_assign: dict[UOp, UOp] = {} assign_rep: dict[UOp, UOp] = {} for u in tsink.toposort(): if u.op is not Ops.AFTER: continue kernel_assign[u.buf_uop] = u for s in u.src[1].src: # TODO: this is probably broken for MSELECT/MSTACK if s.op is not Ops.BUFFER or s is u.buf_uop or (a:=kernel_assign.get(s)) is None: continue if any(x.op is Ops.AFTER and x.buf_uop is s for x in u.toposort()): raise RuntimeError(f"cycle detected in graph, kernel for {u.buf_uop} must either depend on AFTER or BUFFER") assign_rep[a] = kernel_assign[s] = a.replace(src=a.src+(u,)) if assign_rep: tsink = graph_rewrite(tsink, _substitute, ctx=assign_rep, bottom_up=True, name="fix_assign") if getenv("VIZ"): graph_rewrite(tsink, PatternMatcher([]), name="View Kernel Graph") # TODO: we can probably get this earlier sink_tags = [s.tag for s in tsink.src] tsink = graph_rewrite(tsink, _remove_all_tags, name="remove all tags") becomes_map: dict[UOp, UOp] = {} for tag, s in zip(sink_tags, tsink.src): assert tag is not None for a in tag: if a is None: continue becomes_map[uop_list[int(a)]] = s return becomes_map