Release 260111

tinygrad/codegen/gpudims.py

@@ -0,0 +1,94 @@
+import math
+from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType, sint_to_uop
+from tinygrad.helpers import all_int, dedup
+from tinygrad.dtype import dtypes
+from tinygrad.shape.view import get_contraction
+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: dims = dims[::-1]
+  # try to group first: (a, b, c, d) -> (ab, c, d)
+  limited = (grouped if (grouped := _group_dims(dims, max_sizes)) else dims) if max_sizes is not None else dims
+  # check if grouping failed
+  if max_sizes is not None and 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) if max_sizes is not None else dims
+  ret = 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 AssertionError(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)
+  elif len(limited) > len(dims):
+    a, b = len(limited), len(dims)
+    if a == 2 and b == 1: ret = [raw_idxs[0] * limited[1] + raw_idxs[1]]
+    if a == 3 and b == 1: ret = [raw_idxs[0] * (limited[1] * limited[2]) + raw_idxs[1] * limited[2] + raw_idxs[2]]
+    if a == 3 and b == 2: ret = [raw_idxs[0] * limited[1] + raw_idxs[1], raw_idxs[2]]
+  return ret[::-1] if reverse else ret
+
+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:
+    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),
+])