comments

test/test_uops.py

@@ -2,13 +2,13 @@ from typing import Optional, Any
 import unittest, math
 import numpy as np
 from tinygrad.tensor import Tensor, _to_np_dtype
-from tinygrad.helpers import CI, DEBUG, getenv, Timing
+from tinygrad.helpers import CI, DEBUG, getenv, Timing, Context
 from tinygrad.dtype import dtypes, DType, AddrSpace
 from tinygrad.device import Buffer, Device
-from tinygrad.uop.ops import Ops, UOp, UPat, KernelInfo, exec_alu # noqa F401
+from tinygrad.uop.ops import Ops, UOp, UPat, KernelInfo, exec_alu, AxisType
 from tinygrad.uop.spec import shared_spec
 from tinygrad.renderer import ProgramSpec
-from tinygrad.engine.realize import CompiledRunner, get_program
+from tinygrad.engine.realize import CompiledRunner, get_program, get_runner, ExecItem
 from tinygrad.codegen import full_rewrite
 from tinygrad.uop.symbolic import sym
 from tinygrad.device import is_dtype_supported
@@ -39,7 +39,7 @@ def _test_single_value(vals, op, dts):
   output_dtype = dtypes.bool if op in (Ops.CMPLT, Ops.CMPNE) else dts[-1]
   buf_store = uop(uops, Ops.DEFINE_GLOBAL, output_dtype.ptr(), (), 0)
   buf_loads = [uop(uops, Ops.DEFINE_GLOBAL, dtype.ptr(), (), i+1) for i,dtype in enumerate(dts)]
-  loads = (uop(uops, Ops.LOAD, dtype, [buf_loads[i].index(uop(uops, Ops.CONST, dtypes.int32, (), 0), ptr=True)]) for i, dtype in enumerate(dts))
+  loads = (buf_loads[i].index(uop(uops, Ops.CONST, dtypes.int32, (), 0)) for i, dtype in enumerate(dts))
   alu = uop(uops, op, output_dtype, loads)
   out = uop(uops, Ops.STORE, dtypes.void, (buf_store.index(uop(uops, Ops.CONST, dtypes.int32, (), 0), ptr=True), alu))
   buf = Buffer(Device.DEFAULT, 1, output_dtype).allocate()
@@ -56,7 +56,7 @@ def _test_single_value_const(vals, op, dts):
   buf_store = uop(uops, Ops.DEFINE_GLOBAL, output_dtype.ptr(), (), 0)
   loads = (uop(uops, Ops.CONST, dtype, [], a) for a,dtype in zip(vals, dts))
   alu = uop(uops, op, output_dtype, loads)
-  out = uop(uops, Ops.STORE, dtypes.void, (buf_store.index(uop(uops, Ops.CONST, dtypes.int32, (), 0)), alu))
+  out = buf_store[UOp.const(dtypes.int32, 0)].store(alu)
   buf = Buffer(Device.DEFAULT, 1, output_dtype).allocate()
   prg = _uops_to_prg([out])
   prg.exec([buf])
@@ -565,5 +565,45 @@ class TestZeroRange(unittest.TestCase):
       out = Tensor.ones(10, dtype=dtypes.int).contiguous().shrink(((0,v),)).sum()
       self.assertEqual(out.item(), i)
 
+class TestUOpPrograms(unittest.TestCase):
+  def _run(self, prog:UOp, *tensors:Tensor):
+    ExecItem(get_runner(Device.DEFAULT, prog), [t.uop.buffer for t in tensors]).run(wait=True)
+
+  def test_matmul(self):
+    a = Tensor.rand(10,10)
+    b = Tensor.rand(10,10)
+    c = Tensor.empty(10,10)
+    ref = a@b
+    with Context(DEBUG=0): Tensor.realize(a, b, c, ref)
+
+    # C[i,j] = sum_k A[i,k] * B[k,j]
+    # Shapes: A[M,K], B[K,N], C[M,N]
+    M = N = K = 10
+    DT = dtypes.float32
+
+    # Axes: i,j are spatial; k is a reduction axis over the shared dim K
+    i = UOp.range(M, axis_id=0)                             # rows of A/C
+    j = UOp.range(N, axis_id=1)                             # cols of B/C
+    k = UOp.range(K, axis_id=2, axis_type=AxisType.REDUCE)  # reduction over K
+
+    # Placeholders (bind slots explicitly)
+    A = UOp.placeholder(DT, (M, K), slot=0)
+    B = UOp.placeholder(DT, (K, N), slot=1)
+    C = UOp.placeholder(DT, (M, N), slot=2)
+
+    # Zero-init: write a scalar 0 to each (i,j).
+    C = C[i, j].set(0.0)
+
+    # Accumulate: C_after(k) enforces the dependency along the reduction axis
+    C = C[i, j].set(C.after(k)[i, j] + A[i, k] * B[k, j])
+
+    # Finalize the loop nest / schedule in (i, j, k) order
+    prog = C.end(i, j, k)
+
+    # run program
+    self._run(prog.sink(arg=KernelInfo(opts_to_apply=())), a, b, c)
+
+    with Context(DEBUG=0): self.assertLessEqual((c-ref).square().mean().item(), 1e-6)
+
 if __name__ == '__main__':
   unittest.main(verbosity=2)

tinygrad/codegen/__init__.py

@@ -15,7 +15,7 @@ from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_in
   ReduceContext, correct_load_store, pm_render, pm_add_loads
 from tinygrad.codegen.opt.postrange import apply_opts
 from tinygrad.codegen.simplify import pm_simplify_ranges, pm_flatten_range, pm_split_ranges, pm_load_collapse, pm_split_store
-from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen
+from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen, pm_mops
 from tinygrad.codegen.late.linearizer import CFGContext, pm_split_ends, pm_add_control_flow, linearize
 
 def full_rewrite_to_sink(sink:UOp, ren:Renderer|None=None, optimize:bool=True) -> UOp:
@@ -23,6 +23,9 @@ def full_rewrite_to_sink(sink:UOp, ren:Renderer|None=None, optimize:bool=True) -
 
   if SPEC: type_verify(sink, kernel_spec)
 
+  # preprocess
+  sink = graph_rewrite(sink, pm_mops, name="early movement ops")
+
   # first we optimize
   if optimize:
     # collapse loads reduce (indexing by a tensor)

tinygrad/codegen/late/expander.py

@@ -84,7 +84,7 @@ expander = PatternMatcher([
    lambda outer, inner: UOp(Ops.UNROLL, outer.dtype, (inner.src[0],), inner.arg+outer.arg)),
   # do expansion
   (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.GEP, Ops.WMMA, Ops.LOAD, Ops.STORE, Ops.INDEX, Ops.BUFFERIZE,
-         Ops.VECTORIZE, Ops.REDUCE, Ops.END), name="root", custom_early_reject=set([Ops.UNROLL])), do_expand),
+         Ops.VECTORIZE, Ops.REDUCE, Ops.END, Ops.AFTER), name="root", custom_early_reject=set([Ops.UNROLL])), do_expand),
   (UPat(Ops.CONTRACT, name="con"), do_contract),
   # BARRIERs aren't actually expanded
   (UPat(Ops.BARRIER, src=(UPat(Ops.UNROLL, name="ex"),)),

tinygrad/codegen/opt/postrange.py

@@ -64,7 +64,7 @@ class Scheduler:
     return self.ast.replace(arg=KernelInfo(name=name, applied_opts=tuple(self.applied_opts), dont_use_locals=self.dont_use_locals), tag=1)
 
   def _output_rngs(self) -> list[UOp]:
-    return flatten([list(UOp.sink(*s.src[1:]).ranges) for s in self.ast.src if s.op is Ops.END])
+    return flatten([[r for r in UOp.sink(*s.src[1:]).ranges if r.arg[-1] != AxisType.REDUCE] for s in self.ast.src if s.op is Ops.END])
   def _globalizable_rngs(self) -> list[UOp]:
     ret = self._output_rngs()
     # exclude any output ranges from global that don't appear in all BUFFERIZE

tinygrad/engine/realize.py

@@ -90,7 +90,8 @@ class CompiledRunner(Runner):
 
   def __reduce__(self): return self.__class__, (self.p, self.lib)
 
-  def __call__(self, rawbufs:list[Buffer], var_vals:dict[str, int], wait=False) -> float|None:
+  def __call__(self, rawbufs:list[Buffer], var_vals:dict[str, int]|None=None, wait=False) -> float|None:
+    if var_vals is None: var_vals = {}
     has_local = Device[self.p.device].renderer.has_local
     global_size, local_size = self.p.launch_dims(var_vals)
     if has_local and global_size is not None and local_size is None and all_int(self.p.global_size): # type: ignore[arg-type]

tinygrad/schedule/rangeify.py

@@ -18,6 +18,8 @@ sys.setrecursionlimit(10000)
 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)),  # type: ignore
+  (UPat(Ops.RESHAPE, name="r").after(name="a", allow_any_len=True), lambda r,a: a.replace(src=(r.src[0],)+a.src[1:]).reshape(r.shape)),
+  (UPat(Ops.RESHAPE, name="r").end(name="a", allow_any_len=True), lambda r,a: a.replace(src=(r.src[0],)+a.src[1:])),
 ])
 
 # *****************

tinygrad/uop/ops.py

@@ -341,7 +341,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def detach(self): return UOp(Ops.DETACH, self.dtype, (self,))
   def index(self, *srcs:UOp|None, ptr=False, **kwargs):
     return UOp(Ops.INDEX, kwargs.pop("dtype", self.dtype if ptr else self.dtype.base), (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
-  def __getitem__(self, *idx): return self.index(*idx)
+  def __getitem__(self, idx): return self.index(*argfix(idx))
   def const_like(self, b:ConstLike):
     # constants can optionally have a DEVICE source
     return UOp.const(self.dtype, b, device=self._device, shape=self._shape)
@@ -390,10 +390,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if shape is not None: ret = ret.reshape((1,)*len(shape)).expand(shape)
     return ret
   @staticmethod
-  def range(end:sint, *arg, dtype=dtypes.index, src=(), **kwargs):
-    if len(arg) == 0: raise RuntimeError("range needs an arg")
-    if len(arg) == 1: arg = arg+(AxisType.LOOP,)
-    return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end, dtype),)+src, arg=arg, **kwargs)
+  def range(end:sint, axis_id, axis_type=AxisType.LOOP, *arg, dtype=dtypes.index, src=(), **kwargs):
+    return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end, dtype),)+src, arg=(axis_id, axis_type)+arg, **kwargs)
   @staticmethod
   def special(end:sint, name:str, dtype=dtypes.index): return UOp(Ops.SPECIAL, dtype=dtype, src=(sint_to_uop(end, dtype),), arg=name)
   def r(self, op:Ops, axis:tuple[int, ...]):
@@ -745,6 +743,18 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
 
   def pyrender(self): return pyrender(self)
 
+  # *** uop high level syntactic sugar ***
+
+  @staticmethod
+  def placeholder(dtype:DType, shape:tuple[int, ...], slot:int):
+    ret = UOp(Ops.DEFINE_GLOBAL, dtype.ptr(prod(shape)), arg=slot)
+    if len(shape) > 1: ret = ret.reshape(shape)
+    return ret
+
+  # set is store+after
+  def set(self:UOp, val:UOp|ConstType):
+    return self.src[0].after(self.store(UOp.const(self.dtype, val) if not isinstance(val, UOp) else val))
+
 @dataclass(frozen=True)
 class KernelInfo:
   name: str = "test"            # name of the kernel
@@ -873,6 +883,7 @@ class UPat(MathTrait):
   def broadcast(self, **kwargs): return UPat(Ops.VECTORIZE, self.dtype, src=self, **kwargs)
   def contiguous(self, *args, **kwargs): return UPat(Ops.CONTIGUOUS, dtype=self.dtype, src=(self,)+args, **kwargs)
   def after(self, *src:UPat, **kwargs): return UPat(Ops.AFTER, self.dtype, (self,)+src, **kwargs)
+  def end(self, *src:UPat, **kwargs): return UPat(Ops.END, self.dtype, (self,)+src, **kwargs)
 
   def const_like(self, b:ConstLike): return UPat.const(self.dtype, cast(ConstType, b))
   def alu(self, op:Ops, *src:UPat):

tinygrad/uop/spec.py

@@ -115,7 +115,7 @@ shared_codegen_spec = PatternMatcher([
   (UPat(Ops.DEFINE_REG, src=()), lambda: True),
 
   # allow AFTER on buffers, GROUP anywhere
-  (UPat(Ops.AFTER, src=(UPat(GroupOp.Defines),), allow_any_len=True), lambda: True),
+  (UPat(Ops.AFTER, src=(UPat(GroupOp.Defines|{Ops.AFTER}),), allow_any_len=True), lambda: True),
   (UPat(Ops.GROUP, dtypes.void), lambda: True),
 
   # RANGE/SPECIAL define loops, END closes them
@@ -141,7 +141,7 @@ shared_codegen_spec = PatternMatcher([
   (UPat((Ops.CUSTOMI, Ops.CUSTOM, Ops.PRECAST)), lambda: True),
 
   # INDEX
-  (UPat(GroupOp.Defines, name="buf").or_after().index(UPat.var("idx")), validate_index),
+  (UPat(GroupOp.Defines|{Ops.AFTER}, name="buf").index(UPat.var("idx")), validate_index),
 
   # SPECIAL
   (UPat(Ops.SPECIAL, src=(UPat.var("x", (dtypes.index, dtypes.int32)),), name="s"), lambda s,x: s.dtype == x.dtype and isinstance(s.arg, str)),
@@ -150,11 +150,31 @@ shared_codegen_spec = PatternMatcher([
   (UPat(Ops.BARRIER, dtypes.void, src=(UPat(),)), lambda: True),
 ])
 
+# ***** UOp spec in kernel graph *****
+
+kernel_spec = PatternMatcher([
+  # RESHAPE (but only RESHAPE) is allowed here
+  (UPat(Ops.RESHAPE, name="mv", src=(UPat.var("x"), UPat(dtype=dtypes.index))), lambda mv,x: True),
+  (UPat(Ops.AFTER, src=(UPat(Ops.RESHAPE),), allow_any_len=True), lambda: True),
+
+  # index is allowed here
+  (UPat(GroupOp.Elementwise|{Ops.CONST, Ops.RANGE, Ops.DEFINE_VAR}, dtype=dtypes.index), lambda: True),
+
+  # END can end multiple axes here
+  (UPat(Ops.END, src=(UPat(), UPat()), allow_any_len=True, dtype=dtypes.void), lambda: True),
+
+  # bufferize can be on anything
+  (UPat(Ops.BUFFERIZE, src=(UPat(),), allow_any_len=True, name="x"), lambda x: True),
+
+  # reduce must be on ranges
+  (UPat(Ops.REDUCE, src=(UPat(),), allow_any_len=True, name="x"), lambda x: all(y.dtype == dtypes.index for y in x.src[1:])),
+])+shared_codegen_spec+shared_spec
+
 # ***** UOp spec in linearized programs *****
 
 program_spec = PatternMatcher([
   # INDEX with a gate as third src
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines, name="buf").or_after(), UPat.var("idx"), UPat.var("gate", dtype=dtypes.bool))), validate_index),
+  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines|{Ops.AFTER}, name="buf"), UPat.var("idx"), UPat.var("gate", dtype=dtypes.bool))), validate_index),
 
   # LOAD (idx, alt_value), LOAD can have an alt value, but only if the index has a gate
   (UPat().index(UPat(), UPat(dtype=dtypes.bool)).or_casted().load(UPat()), lambda: True),
@@ -173,22 +193,6 @@ program_spec = PatternMatcher([
   (UPat(Ops.ENDIF, dtype=dtypes.void, src=(UPat(Ops.IF),)), lambda: True),
 ])+shared_codegen_spec+shared_spec
 
-# ***** UOp spec in kernel graph *****
-
-kernel_spec = PatternMatcher([
-  # index is allowed here
-  (UPat(GroupOp.Elementwise|{Ops.CONST, Ops.RANGE, Ops.DEFINE_VAR}, dtype=dtypes.index), lambda: True),
-
-  # END can end multiple axes here
-  (UPat(Ops.END, src=(UPat(), UPat()), allow_any_len=True, dtype=dtypes.void), lambda: True),
-
-  # bufferize can be on anything
-  (UPat(Ops.BUFFERIZE, src=(UPat(),), allow_any_len=True, name="x"), lambda x: True),
-
-  # reduce must be on ranges
-  (UPat(Ops.REDUCE, src=(UPat(),), allow_any_len=True, name="x"), lambda x: all(y.dtype == dtypes.index for y in x.src[1:])),
-])+shared_codegen_spec+shared_spec
-
 # *** this spec should match all UOps ever created ***
 
 full_spec = PatternMatcher([

tinygrad/viz/serve.py

@@ -155,7 +155,7 @@ def timeline_layout(dev_events:list[tuple[int, int, float, DevEvent]], start_ts:
       name = ctxs[ref]["name"]
       if isinstance(p:=trace.keys[ref].ret, ProgramSpec) and (ei:=exec_points.get(p.name)) is not None:
         info = f"{sym_infer(p.estimates.ops, ei.arg['var_vals'])/(t:=dur*1e3):.2f} GFLOPS {sym_infer(p.estimates.mem, ei.arg['var_vals'])/t:4.1f}"+ \
-               f"|{sym_infer(p.estimates.lds,ei.arg['var_vals'])/t:.1f} GB/s\n{[str(m) for m in ei.arg['metadata']]}"
+               f"|{sym_infer(p.estimates.lds,ei.arg['var_vals'])/t:.1f} GB/s\n{[str(m) for m in (ei.arg['metadata'] or ())]}"
         key = ei.key
     elif isinstance(e.name, TracingKey):
       name = e.name.display_name