reorder

extra/gemm/amd_uop_matmul.py

@@ -29,6 +29,8 @@ def hl_spec_kernel3():
   Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1).view(ShapeTracker.from_shape((BK*BN,)))
   A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((nbIterWaveM * TM,)))
   B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), arg=1).view(ShapeTracker.from_shape((nbIterWaveN * TN,)))
+  acc = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM * nbIterWaveN * TN, AddrSpace.REG), arg=2) \
+    .view(ShapeTracker.from_shape((nbIterWaveM * TM * nbIterWaveN * TN,)))
 
   # shape buffers. TODO: permutes
   full_shape = (N//BM, nbIterWaveM, BM//(nbIterWaveM * TM), TM, N//BN, nbIterWaveN, BN//(nbIterWaveN * TN), TN, N//BK, BK)
@@ -40,9 +42,14 @@ def hl_spec_kernel3():
   A_col = A_col.reshape((1, nbIterWaveM, 1, TM, 1, 1, 1, 1, 1, 1)).expand(full_shape)
   B_row = B_row.reshape((1, 1, 1, 1, 1, nbIterWaveN, 1, TN, 1, 1)).expand(full_shape)
 
-  #out = (a.load() * b.load()).r(Ops.ADD, (8, 9))
+  acc = acc.reshape((1, nbIterWaveM, 1, TM, 1, nbIterWaveN, 1, TN, 1, 1)).expand(full_shape[:-2]+(1,1))
-  out = (As.load(As.store(a.load())) * Bs.load(Bs.store(b.load()))).r(Ops.ADD, (8, 9))
+
-  #out = (A_col.load(A_col.store(As.load(As.store(a.load())))) * B_row.load(B_row.store(Bs.load(Bs.store(b.load()))))).r(Ops.ADD, (8, 9))
+  #out = a.load() * b.load()
+  #out = As.load(As.store(a.load())) * Bs.load(Bs.store(b.load()))
+  out = A_col.load(A_col.store(As.load(As.store(a.load())))) * B_row.load(B_row.store(Bs.load(Bs.store(b.load()))))
+
+  #out = out.r(Ops.ADD, (8, 9))
+  out = UOp(Ops.REDUCE_INTO, out.dtype, (acc, out), Ops.ADD)
 
   axis_types = (
     AxisType.GLOBAL, AxisType.UPCAST, AxisType.LOCAL, AxisType.UPCAST,
@@ -51,6 +58,7 @@ def hl_spec_kernel3():
 
   from tinygrad.opt.kernel import axis_colors
   shape = '_'.join([colored(str(s), axis_colors[at]) for s,at in zip(full_shape, axis_types)])
+  print(shape)
   sink = c.store(out).sink(arg=KernelInfo(name="tg_"+shape, axis_types=axis_types))
   sink = graph_rewrite(sink, merge_views)
   return sink
@@ -144,8 +152,8 @@ def hand_spec_kernel3():
 
   # do the GEMM math
   iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 8)
-  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 9)
+  yt = UOp.range(dtypes.int, TM, 9)
-  yt = UOp.range(dtypes.int, TM, 10)
+  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 10)
   xt = UOp.range(dtypes.int, TN, 11)
   x = iterWaveN * TN + xt
   y = iterWaveM * TM + yt
@@ -155,8 +163,8 @@ def hand_spec_kernel3():
 
   # store c_regs into c
   iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 12)
-  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 13)
+  yt = UOp.range(dtypes.int, TM, 13)
-  yt = UOp.range(dtypes.int, TM, 14)
+  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 14)
   xt = UOp.range(dtypes.int, TN, 15)
   xOut = blockIdx_x * BN + waveIdx * WN + iterWaveN * SUBWN + TN * idxInWave
   yOut = blockIdx_y * BM + waveIdy * WM + iterWaveM * SUBWM + TM * idyInWave
@@ -170,6 +178,7 @@ if __name__ == "__main__":
   hprg = hl_spec_kernel3() if getenv("HL") else hand_spec_kernel3()
   prg = get_program(hprg, Device.default.renderer)
   print(prg.src)
+  exit(0)
   hrunner = CompiledRunner(prg)
 
   a = Tensor.randn(N, N).realize()

test/test_define_reg.py

@@ -28,5 +28,15 @@ class TestDefineReg(unittest.TestCase):
   @unittest.skipIf(getenv("PTX"), "ptx needs regs to be unrolled")
   def test_simple_loop(self): self.test_simple(AxisType.LOOP)
 
+  def test_reduce_into(self):
+    N = 16
+    b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N*N), arg=0).view(ShapeTracker.from_shape((N,N,1)))
+    a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N*N), arg=1).view(ShapeTracker.from_shape((N,N,N)))
+    a_reg = UOp(Ops.DEFINE_REG, dtypes.float.ptr(N, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((N,N,1), (0,1,0)))
+    ret = UOp(Ops.REDUCE_INTO, dtypes.float, src=(a_reg, a.load()), arg=Ops.ADD)
+    sink = b.store(ret).sink(arg=KernelInfo(name="regcopy"))
+    prg = get_program(sink, Device.default.renderer)
+    print(prg.src)
+
 if __name__ == '__main__':
   unittest.main()

tinygrad/codegen/lowerer.py

@@ -2,20 +2,34 @@
 from dataclasses import dataclass
 from typing import cast
 from tinygrad.dtype import dtypes, PtrDType, AddrSpace
-from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType
+from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType, graph_rewrite
 from tinygrad.helpers import prod, partition, flatten
 
 # ***** indexing *****
 
 @dataclass
 class IndexContext:
+  axis_types: list[AxisType]
   idxs: list[UOp]
-  ridxs: list[UOp]
+  start: int = 0
+  #ridxs: list[UOp]
+
+def shape_to_idx(s, axis_types, start=0, allow_unroll=False):
+  idxs = []
+  for i, (s, at) in enumerate(zip(s, axis_types)):
+    if at in (AxisType.UPCAST, AxisType.UNROLL) and allow_unroll:
+      assert isinstance(s, int), "needs to be int to upcast/unroll"
+      idxs.append(UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s), tuple(range(s))),), ((i,s),)))
+    else:
+      # all others are RANGES
+      idxs.append(UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), start+i))
+  return idxs
 
 def get_index(ast:UOp) -> IndexContext:
   axis_types = ast.arg.axis_types if isinstance(ast.arg, KernelInfo) else ()
   if len(ast.full_shape) != len(axis_types): axis_types = (AxisType.LOOP,)*len(ast.full_shape)
 
+  """
   # indexes
   idxs = []
   for i, (s, at) in enumerate(zip(ast.full_shape, axis_types)):
@@ -31,34 +45,85 @@ def get_index(ast:UOp) -> IndexContext:
   for i, (s, at) in enumerate(zip(ast.full_shape, axis_types)):
     if at == AxisType.GROUP_REDUCE:
       ridxs[i] = UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), 1000+i)
+  """
 
-  return IndexContext(idxs, ridxs)
+  return IndexContext(axis_types, []) #idxs, ridxs)
 
 # ***** lowering (given index) *****
 
+def lower_reduce_into(ctx: IndexContext, x: UOp):
+  # x.src[1] is the big
+  axis_arg = [i for i,(s0,s1) in enumerate(zip(x.src[0].shape, x.src[1].shape)) if s0 != s1]
+  reduce_idxs = shape_to_idx([s for i,s in enumerate(x.src[1].shape) if i in axis_arg],
+                             [s for i,s in enumerate(ctx.axis_types) if i in axis_arg], ctx.start)
+  print("reduce into", [x.arg for x in reduce_idxs])
+  new_idxs = shape_to_idx(x.src[0].shape[:-len(reduce_idxs)], ctx.axis_types[:-len(reduce_idxs)], ctx.start+len(reduce_idxs)) + reduce_idxs
+
+  idx, valid = x.src[0].arg.to_indexed_uops(new_idxs)
+  used_idxs = [x for x in UOp.sink(idx, valid).toposort() if x in new_idxs]
+
+  real_new_idxs = []
+  for i in range(len(x.src[0].shape)):
+    if new_idxs[i] in used_idxs or new_idxs[i] in reduce_idxs or len(ctx.idxs) <= i: real_new_idxs.append(new_idxs[i])
+    else: real_new_idxs.append(ctx.idxs[i])
+  non_replaced = [x for x in real_new_idxs if x not in ctx.idxs]
+
+  acc = x.src[0].load(*reduce_idxs).alu(x.arg, x.src[1])
+
+  lc = IndexContext(ctx.axis_types, tuple(real_new_idxs), ctx.start+len(new_idxs))
+  from tinygrad.codegen.lowerer import pm_lowerer  # TODO: better way to do this?
+  ret = graph_rewrite(acc, pm_lowerer, lc, name="subreduce", bottom_up=True)
+  ctx.start = lc.start
+
+  red = x.src[0].src[0].index(idx, valid).store(ret, *used_idxs, *reduce_idxs)
+  return x.src[0].load(red)
+
 def lower_reduce_axis(ctx: IndexContext, x: UOp):
+  new_idx = shape_to_idx([s for i,s in enumerate(x.src[0].shape) if i in x.axis_arg],
+                         [s for i,s in enumerate(ctx.axis_types) if i in x.axis_arg], ctx.start)
+  full_new_idx = list(ctx.idxs)
+  for i,s in zip(x.axis_arg, new_idx):
+    #assert len(full_new_idx) == i, f"len(full_new_idx) = {len(full_new_idx)}, trying to place {i}"
+    #full_new_idx.append(s)
+    full_new_idx[i] = s
+
+  lc = IndexContext(ctx.axis_types, tuple(full_new_idx), ctx.start+len(new_idx))
+  from tinygrad.codegen.lowerer import pm_lowerer  # TODO: better way to do this?
+  ret = graph_rewrite(x.src[0], pm_lowerer, lc, name="subreduce", bottom_up=True)
+  ctx.start = lc.start
+
   # NOTE: always using ridxs is fine here
-  reduce_range, reduce_expand = partition([ctx.ridxs[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
+  reduce_range, reduce_expand = partition([full_new_idx[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
   assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand} for {x.axis_arg}"
-  ret = x.src[0]
   if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
     ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis))
   # REDUCE supports both "horizontal" reduction and range reduction. the horizontal elements are taken in the nearest group
   return UOp(Ops.REDUCE, x.dtype, (ret,)+tuple(reduce_range), x.arg[0])
 
 def lower_load(ctx: IndexContext, x: UOp, buf: UOp):
-  idx, valid = x.st_arg.to_indexed_uops(ctx.ridxs if buf.op is Ops.DEFINE_LOCAL else ctx.idxs)
+  print("lower_load", [x.arg for x in ctx.idxs])
-  barrier = tuple([y.barrier() if buf.op is Ops.DEFINE_LOCAL else y for y in x.src[1:]])
+  idx, valid = x.st_arg.to_indexed_uops(ctx.idxs)
-  return UOp(Ops.LOAD, x.dtype, (buf.index(idx, valid),) + barrier)
+  return UOp(Ops.LOAD, x.dtype, (buf.index(idx, valid),) + x.src[1:])
+
+  #barrier = tuple([y.barrier() if buf.op is Ops.DEFINE_LOCAL else y for y in x.src[1:]])
 
 def lower_store(ctx: IndexContext, x: UOp, buf: UOp):
   assert x.src[1].shape == x.src[0].shape, f"shape mismatch on store {x.src[1].shape} != {x.src[0].shape}"
-  idx, valid = x.st_arg.to_indexed_uops(ctx.idxs)
+  new_idxs = shape_to_idx(x.src[0].shape, ctx.axis_types, ctx.start)
-  if cast(PtrDType, buf.dtype).addrspace == AddrSpace.GLOBAL:
+  idx, valid = x.st_arg.to_indexed_uops(new_idxs)
-    # NOTE: only store the local reduceop in the threads that are actually doing the reduce
+  used_idxs = [x for x in UOp.sink(idx, valid).toposort() if x in new_idxs]
-    for oidx, ridx in zip(ctx.idxs, ctx.ridxs):
+  real_new_idxs = []
-      if oidx is not ridx: valid = valid * oidx.eq(0)
+  for i in range(len(x.src[0].shape)):
-  return buf.index(idx, valid).store(x.src[1], *[x for x in UOp.sink(idx, valid).toposort() if x.op is Ops.RANGE])
+    if new_idxs[i] in used_idxs or len(ctx.idxs) <= i:
+      real_new_idxs.append(new_idxs[i])
+    else:
+      real_new_idxs.append(ctx.idxs[i])
+  print("got", len(real_new_idxs), len(used_idxs))
+  lc = IndexContext(ctx.axis_types, tuple(real_new_idxs), ctx.start+len(new_idxs))
+  from tinygrad.codegen.lowerer import pm_lowerer  # TODO: better way to do this?
+  stored = graph_rewrite(x.src[1], pm_lowerer, lc, name="substore", bottom_up=True)
+  ctx.start = lc.start
+  return buf.index(idx, valid).store(stored, *[x for x in used_idxs if x.op is Ops.RANGE])
 
 def lower_const(ctx:IndexContext, view:UOp, c:UOp):
   if all(x.mask is None for x in view.arg.views): return c
@@ -73,6 +138,7 @@ pm_lowerer = PatternMatcher([
   (UPat(Ops.VALID, src=(UPat(Ops.VIEW, name="v"),)).where(UPat.cvar("c"), UPat(Ops.CONST, arg=0)), lambda c,v: c.replace(src=()).view(v.arg)),
 
   # reduce/view_const
+  (UPat(Ops.REDUCE_INTO, name="x"), lower_reduce_into),
   (UPat(Ops.REDUCE_AXIS, name="x"), lower_reduce_axis),
   (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="c"),), name="view"), lower_const),
   # rewrite LOAD/STORE VIEW to LOAD/STORE with indexed

tinygrad/uop/__init__.py

@@ -40,7 +40,7 @@ class Ops(FastEnum):
   SPECIAL = auto()
 
   # reduce
-  REDUCE_AXIS = auto(); REDUCE = auto(); ALLREDUCE = auto() # noqa: E702
+  REDUCE_AXIS = auto(); REDUCE_INTO = auto(); REDUCE = auto(); ALLREDUCE = auto() # noqa: E702
 
   # optimization helper ops
   UNROLL = auto(); CONTRACT = auto(); GEP = auto(); VECTORIZE = auto(); CAT = auto(); PTRCAT = auto() # noqa: E702

tinygrad/uop/ops.py

@@ -138,6 +138,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def st(self) -> ShapeTracker|None:
     if self.op in GroupOp.Block or self.op is Ops.INDEX: return None
     from tinygrad.shape.shapetracker import ShapeTracker
+    if self.op is Ops.REDUCE_INTO: return self.src[0].st
     # VIEW and MovementOps define a new ShapeTracker from the arg
     if self.op is Ops.VIEW: return self.arg
     if self.op in GroupOp.Movement: return unwrap(self.src[0].st).mop(self.op, self.arg)

tinygrad/uop/spec.py

@@ -198,7 +198,7 @@ spec = PatternMatcher([
   # NOTE: for testing, we let sinks be anything
   #(UPat(Ops.SINK, src=UPat(Ops.STORE)), lambda: True),
   (UPat(Ops.SINK, dtypes.void), lambda: True),
-  (UPat((Ops.NOOP, Ops.CUSTOMI, Ops.CUSTOM, Ops.PRECAST)), lambda: True),
+  (UPat((Ops.NOOP, Ops.CUSTOMI, Ops.CUSTOM, Ops.PRECAST, Ops.REDUCE_INTO)), lambda: True),
 
   # PTX LOAD/STORE
   (UPat((Ops.LOAD, Ops.STORE), src=(UPat(dtype=dtypes.int64),), allow_any_len=True), lambda: True),

tinygrad/viz/serve.py

@@ -13,6 +13,7 @@ from tinygrad.dtype import dtypes
 
 uops_colors = {Ops.LOAD: "#ffc0c0", Ops.STORE: "#87CEEB", Ops.CONST: "#e0e0e0", Ops.VCONST: "#e0e0e0", Ops.REDUCE: "#FF5B5B",
                Ops.DEFINE_GLOBAL: "#ffe0b0", Ops.DEFINE_LOCAL: "#ffe0d0", Ops.DEFINE_REG: "#f0ffe0", Ops.REDUCE_AXIS: "#FF6B6B",
+               Ops.REDUCE_INTO: "#FF6B6B",
                Ops.RANGE: "#c8a0e0", Ops.ASSIGN: "#909090", Ops.BARRIER: "#ff8080", Ops.IF: "#c8b0c0", Ops.SPECIAL: "#c0c0ff",
                Ops.INDEX: "#e8ffa0", Ops.WMMA: "#efefc0", Ops.VIEW: "#C8F9D4", Ops.MULTI: "#f6ccff", Ops.KERNEL: "#3e7f55",
                **{x:"#D8F9E4" for x in GroupOp.Movement}, **{x:"#ffffc0" for x in GroupOp.ALU}, Ops.THREEFRY:"#ffff80", Ops.BUFFER_VIEW: "#E5EAFF",