dragonpilot/tinygrad_repo/tinygrad/codegen/gpudims.py

import math, functools, operator
from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType, sint_to_uop
from tinygrad.helpers import all_int, dedup, get_contraction
from tinygrad.dtype import dtypes, AddrSpace, Invalid
from tinygrad.renderer import Renderer

def _group_dims(dims:tuple[sint, ...], max_sizes:tuple[int, ...]):
  # TODO: symbolic shape
  if not all_int(dims): return dims
  while len(dims) > len(max_sizes) or any(d > m for d,m in zip(dims, max_sizes)):
    for i,m in enumerate(max_sizes):
      if i < (len(dims)-1) and dims[i] * dims[i+1] <= m:
        dims = dims[:i] + (dims[i]*dims[i+1],) + dims[i+2:]
        break
    else: return None
  return dims

def _split_dims(dims, max_sizes):
  if all(d <= m for d,m in zip(dims, max_sizes)): return dims
  _dims = list(dims) + [1]*(3-len(dims))
  for i in range(len(_dims)):
    while _dims[i] > max_sizes[i]:
      div = next((d for d in range(2, math.ceil(math.sqrt(_dims[i])) + 1) if (_dims[i] % d) == 0), 1)
      if div == 1: raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
      _dims[i], _dims[(i+1)%len(_dims)] = _dims[i]//div, _dims[(i+1)%len(_dims)]*div
  return tuple(_dims[:2] if _dims[2] == 1 else _dims[0] if _dims[1:3] == [1,1] else _dims)

def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|None, reverse=False) -> list[UOp]:
  if reverse: return get_grouped_dims(prefix, dims[::-1], max_sizes)[::-1]
  if max_sizes is None: limited = dims
  else:
    # try to group first: (a, b, c, d) -> (ab, c, d)
    limited = grouped if (grouped := _group_dims(dims, max_sizes)) else dims
    # check if grouping failed
    if len(limited) > len(max_sizes): raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
    # try to split up dims: (a,) -> (b, c)
    if limited == dims: limited = _split_dims(dims, max_sizes)
  raw_idxs = [UOp(Ops.SPECIAL, dtypes.index, (sint_to_uop(s),), (f"{prefix}{i}")) for i,s in enumerate(limited)]
  if len(limited) < len(dims):
    ret = []
    if (contraction:=get_contraction(dims, limited)) is None: raise RuntimeError(f"get_contraction should not be None {dims=} {limited=}")
    for idx, contraction_group in zip(raw_idxs, contraction):
      for c in contraction_group[:-1]:
        ret.append(idx % dims[c])
        idx //= dims[c]
      ret.append(idx)
    return ret
  elif (a:=len(limited)) > (b:=len(dims)):
    if a == 2 and b == 1: return [raw_idxs[0] * limited[1] + raw_idxs[1]]
    if a == 3 and b == 1: return [(raw_idxs[0] * limited[1] + raw_idxs[1]) * limited[2] + raw_idxs[2]]
    if a == 3 and b == 2: return [raw_idxs[0] * limited[1] + raw_idxs[1], raw_idxs[2]]
  elif limited != dims:
    # Convert to 1D
    flat = raw_idxs[0]*limited[1]+raw_idxs[1] if len(dims) == 2 else raw_idxs[0]*(limited[1]*limited[2])+raw_idxs[1]*limited[2]+raw_idxs[2]
    # Get back original indices from 1D
    return [flat//dims[1], flat%dims[1]] if len(dims) == 2 else [flat//(dims[2]*dims[1]), (flat//dims[2])%dims[1], flat%dims[2]]
  return raw_idxs

def add_gpudims(ctx:Renderer, s:UOp):
  if s.arg is None: return None
  s_topo = list(s.toposort())
  if any(x.op is Ops.SPECIAL for x in s_topo): return None

  # get ranges
  all_ranges = {x.arg[0:-1]:x for x in s_topo if x.op is Ops.RANGE}

  # extract global/local dims
  global_dims = sorted(dedup([x.arg[0:-1] for x in all_ranges.values() if x.arg[-1] in (AxisType.GLOBAL, AxisType.THREAD)]))
  local_dims = sorted(dedup([x.arg[0:-1] for x in all_ranges.values() if x.arg[-1] in (AxisType.WARP, AxisType.LOCAL, AxisType.GROUP_REDUCE)]))
  if not global_dims and not local_dims: return None

  # get global and local shape
  ranges = [all_ranges[r] for r in global_dims+local_dims if r in all_ranges]
  global_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg[0:-1] in global_dims])
  local_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg[0:-1] in local_dims])

  # get the idxs
  ki: KernelInfo = s.arg
  if ki.dont_use_locals:
    assert not local_dims, "can't use locals if there's no local dims"
    idxs = get_grouped_dims("idx", global_shape, ctx.global_max, reverse=True)
  else:
    # define indexes for GPU-like execution
    idxs = get_grouped_dims("gidx", global_shape, ctx.global_max, reverse=True) + get_grouped_dims("lidx", local_shape, ctx.local_max)

  # apply to multiple ranges
  subs = {}
  for r in s_topo:
    # look for local INDEXes that are not used in the GLOBAL store, then add them as an INVALID
    if r.op is Ops.STORE and r.buf_target().ptrdtype.addrspace == AddrSpace.GLOBAL:
      idx = r.src[0]
      missing_locals = [all_ranges[rng] for rng in local_dims if all_ranges[rng] not in idx.ranges]
      if len(missing_locals):
        assert len(idx.src) == 2, "index has 2 sources"
        mask: UOp = functools.reduce(operator.and_, [x.eq(0) for x in missing_locals])
        subs[idx] = idx.replace(src=(idx.src[0], mask.broadcast(idx.src[1].dtype.count).where(idx.src[1], Invalid)))
    if r.op is not Ops.RANGE: continue
    try:
      ii = (global_dims+local_dims).index(r.arg[0:-1])
      if r.arg[1] == AxisType.REDUCE: continue
      subs[r] = idxs[ii]
    except ValueError: continue
  return s.substitute(subs)

pm_add_gpudims = PatternMatcher([
  # add gpudims must be last
  (UPat(Ops.SINK, name="s"), add_gpudims),
])