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143 lines
7.6 KiB
143 lines
7.6 KiB
# the job of the lowerer is to do indexing
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import math
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from dataclasses import dataclass
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from typing import cast
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from tinygrad.dtype import dtypes, PtrDType
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from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint, sint_to_uop
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from tinygrad.renderer import Renderer
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from tinygrad.helpers import all_int, prod, partition, flatten, unwrap
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from tinygrad.shape.view import get_contraction
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# ***** indexing *****
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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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@dataclass
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class IndexContext:
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idxs: list[UOp]
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ridxs: list[UOp]
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def get_index(ast:UOp, opts:Renderer) -> IndexContext:
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ki = ast.arg if isinstance(ast.arg, KernelInfo) else KernelInfo()
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# NOTE: assumes the shape is <global dims> <local dims> <group_for_reduces> <reduces> <upcasts/unrolls>
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full_shape = ast.full_shape
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first_upcasted = len(full_shape)-ki.upcasted
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# if there's no reduce, this is first_upcasted. assumes reduces are at the end
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first_reduce = min([first_upcasted]+flatten(x.axis_arg for x in ast.toposort() if x.op is Ops.REDUCE_AXIS))
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local_loads = [x for x in ast.toposort() if x.op is Ops.LOAD and x.src[0].base.op is Ops.DEFINE_LOCAL]
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# NOTE: sum up the reduced axes looking across all local loads, yields the number of grouped reduces
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group_for_reduces = sum([any(l.st_arg.shape[i]!=ast.src[0].st_arg.shape[i] for l in local_loads) for i in range(first_reduce,first_upcasted)])
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global_dims = first_reduce-ki.local_dims
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if opts.has_local:
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if ki.dont_use_locals:
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assert ki.local_dims == 0, "can't use locals if there's no local dims"
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idxs = get_grouped_dims("idx", full_shape[:global_dims], opts.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", full_shape[:global_dims], opts.global_max, reverse=True) + \
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get_grouped_dims("lidx", full_shape[global_dims:first_reduce+group_for_reduces], opts.local_max)
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else:
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# all loops are RANGES
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idxs = [UOp(Ops.RANGE, dtypes.int, (sint_to_uop(g),), i) for i,g in enumerate(full_shape[:first_reduce])]
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# reduce loops
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idxs += [UOp(Ops.RANGE, dtypes.int, (sint_to_uop(g),), i)
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for i,g in enumerate(full_shape[first_reduce+group_for_reduces:first_upcasted], start=first_reduce+group_for_reduces)]
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# upcast loops
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for i,g in enumerate(full_shape[first_upcasted:], start=first_upcasted):
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assert isinstance(g, int), "needs to be int to upcast/unroll"
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idxs.append(UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(g), tuple(range(g))),), ((i,g),)))
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# late indexes (group for reduce)
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ridxs = idxs[:]
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for a in range(first_reduce, first_reduce+group_for_reduces):
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ridxs[a] = UOp(Ops.RANGE, dtypes.int, (sint_to_uop(full_shape[a]),), 1000+a)
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return IndexContext(idxs, ridxs)
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# ***** lowering (given index) *****
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def lower_reduce_axis(ctx: IndexContext, x: UOp):
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# NOTE: always using ridxs is fine here
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reduce_range, reduce_expand = partition([ctx.ridxs[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
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assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand} for {x.axis_arg}"
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alu_op: Ops = x.arg[0]
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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))
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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 UOp(Ops.REDUCE, x.dtype, (ret,)+tuple(reduce_range), alu_op)
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def lower_load_store(ctx: IndexContext, x: UOp, buf: UOp):
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idx, valid = x.st_arg.to_indexed_uops(ctx.ridxs if x.op is Ops.LOAD and buf.op is Ops.DEFINE_LOCAL else ctx.idxs)
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if x.op is Ops.LOAD:
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barrier = (UOp(Ops.BARRIER, dtypes.void, (x.src[1],)),) if buf.op is Ops.DEFINE_LOCAL else ()
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return UOp(Ops.LOAD, x.dtype, (buf.index(idx, valid),) + barrier)
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# NOTE: only store the local reduceop in the threads that are actually doing the reduce
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if cast(PtrDType, buf.dtype).local and x.src[1].op is Ops.REDUCE:
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reduce_input = x.src[1].src[0]
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store_back = reduce_input.op is Ops.LOAD and cast(PtrDType, reduce_input.src[0].dtype).local
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else: store_back = False
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# NOTE: If we're storing the reduced value back into each thread, need to zero-out the reduced axes
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if store_back: idx, _ = x.st_arg.to_indexed_uops([u.const_like(0) if u in x.src[1].src else u for u in ctx.idxs])
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if (not cast(PtrDType, buf.dtype).local) or store_back:
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for oidx, ridx in zip(ctx.idxs, ctx.ridxs):
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if oidx is not ridx: valid = valid * oidx.eq(0)
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return UOp(Ops.STORE, dtypes.void, (buf.index(idx, valid), x.src[1]))
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def lower_const(x:UOp):
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assert all(v.mask is None for v in unwrap(x.st).views), f"VIEW in CONST/DEFINE_VAR source must be unmasked, got {x.st}"
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return x.replace(src=())
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pm_lowerer = PatternMatcher([
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(UPat(Ops.REDUCE_AXIS, name="x"), lower_reduce_axis),
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(UPat((Ops.CONST, Ops.DEFINE_VAR), src=(UPat(Ops.VIEW),), name="x"), lower_const),
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(UPat(Ops.VALID, src=(UPat(Ops.VIEW),), name="x"), lambda ctx,x: x.st_arg.to_indexed_uops(ctx.idxs)[1]),
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# rewrite LOAD/STORE VIEW to LOAD/STORE with indexed
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(UPat((Ops.LOAD, Ops.STORE), src=(UPat.var("buf").view(),), allow_any_len=True, name="x"), lower_load_store),
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(UPat(Ops.INDEX, src=(UPat.var("b"), UPat.var("idx"), UPat.const(dtypes.bool, True))), lambda b, idx: b.index(idx)),
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])
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