never mind, i don't like this

* write scan in uops

* ops range

* no need for variable

* meh, later

* shorter

extra/sqtt/active_sqtt_parse.py

@@ -8,17 +8,19 @@ os.environ["AMD_LLVM"] = "0"
 from dataclasses import replace
 import atexit, contextlib
 from tinygrad import Tensor
-from tinygrad.helpers import system, getenv
+from tinygrad.helpers import system, OSX
 from tinygrad.runtime.ops_amd import AMDProgram
 from extra.sqtt.roc import decode, WaveExec, ProfileSQTTEvent
 from tinygrad.device import Device, ProfileDeviceEvent
 
 from extra.sqtt.attempt_sqtt_parse import parse_sqtt_print_packets
 
-def set_power(x): system(f"sudo /opt/rocm/bin/amd-smi set -l {x}")
-@atexit.register
-def reset_power(): set_power("auto")
-set_power("stable_std")
+# TODO: should really check for AM driver / USB
+if not OSX:
+  def set_power(x): system(f"sudo /opt/rocm/bin/amd-smi set -l {x}")
+  @atexit.register
+  def reset_power(): set_power("auto")
+  set_power("stable_std")
 
 dev = Device["AMD"]
 
@@ -30,9 +32,9 @@ def save_sqtt():
   yield sqtt
   events = dev.profile_events+[ProfileDeviceEvent("AMD", props=dev.device_props())]
 
-  #rctx = decode(events)
-  #assert len(rctx.inst_execs) > 0, "empty sqtt output"
-  #sqtt.update(rctx.inst_execs)
+  rctx = decode(events)
+  assert len(rctx.inst_execs) > 0, "empty sqtt output"
+  sqtt.update(rctx.inst_execs)
 
   for e in events:
     if isinstance(e, ProfileSQTTEvent):

test/external/external_test_example.py

@@ -1,13 +1,14 @@
 import unittest
 from tinygrad import Device
 from tinygrad.tensor import Tensor
-from tinygrad.helpers import getenv, CI
+from tinygrad.helpers import getenv, CI, OSX
 
 def multidevice_test(fxn):
   exclude_devices = getenv("EXCLUDE_DEVICES", "").split(",")
   def ret(self):
     for device in Device._devices:
-      if device in ["REMOTE", "DISK", "NPY", "FAKE", "DSP", "NULL"]: continue
+      # broken on OSX USB AMD, why?
+      if device in ["REMOTE", "DISK", "NPY", "FAKE", "DSP", "NULL"] or (OSX and device in ["AMD"]): continue
       if not CI: print(device)
       if device in exclude_devices:
         if not CI: print(f"WARNING: {device} test is excluded")

test/test_outerworld.py

@@ -50,8 +50,52 @@ class TestOuterRange(unittest.TestCase):
     # 3 matmuls with outer world range
     i = UOp.range(3, -100, AxisType.OUTER)
     vec_i = Tensor(vec.uop.after(i))
-    vi = UOp.variable("i", i.vmin, i.vmax).bind(i)
-    out = Tensor(vec.uop.after(vec_i.uop.store((vec_i.contiguous() @ mats[vi]).uop).end(i)))
+    comp = vec_i.contiguous() @ mats[i]
+    store = vec_i.uop.store(comp.uop).end(i)
+    out = Tensor(vec.uop.after(store))
+    out.realize()
+
+    # TODO: testing allclose
+    assert Tensor.allclose(ref, out, atol=1e-6), f"{ref.numpy()=}, {out.numpy()=}"
+
+class TestOuterScan(unittest.TestCase):
+  def _test_scan(self):
+    vec = Tensor.randn(1, 10).realize()
+    mats = Tensor.randn(3, 10, 10).realize()
+
+    # 3 matmuls in "scan"
+    vec1 = vec @ mats[0]
+    vec2 = vec1 @ mats[1]
+    vec3 = vec2 @ mats[2]
+    ref = Tensor.stack(vec1, vec2, vec3)
+    ref.realize()
+    return vec, mats, ref
+
+  def test_uop_fold_matmul(self):
+    vec, mats, ref = self._test_scan()
+
+    # 3 matmuls with FOLD
+    i = UOp.range(3, -100, AxisType.OUTER)
+    out = Tensor.empty(1, 10)
+    phi = Tensor(i.eq(0).where(vec.uop, out.uop))
+    comp = phi @ mats[i]
+    store = out.uop.store(comp.uop).end(i)
+    out = Tensor(out.uop.after(store))
+    out.realize()
+
+    # TODO: testing allclose
+    assert Tensor.allclose(ref[2], out, atol=1e-6), f"{ref.numpy()=}, {out.numpy()=}"
+
+  def test_uop_scan_matmul(self):
+    vec, mats, ref = self._test_scan()
+
+    # 3 matmuls with SCAN
+    i = UOp.range(3, -100, AxisType.OUTER)
+    out = Tensor.empty(3, 1, 10)
+    phi = Tensor(i.eq(0).where(vec.uop, out[(i-1).maximum(0)].uop))
+    comp = phi @ mats[i]
+    store = out[i].uop.store(comp.uop).end(i)
+    out = Tensor(out.uop.after(store))
     out.realize()
 
     # TODO: testing allclose
@@ -116,5 +160,84 @@ class TestOuterworld(unittest.TestCase):
     out = out.reshape(1, 3).expand(a, 3).contiguous().realize()
     self.assertListEqual([[0,4,8],[4,8,12],[8,12,16]], out.tolist())
 
+class TestVmap(unittest.TestCase):
+  def test_vmap_inner(self):
+    x = Tensor.ones(3, 2).contiguous()
+
+    # vmap across axis 0
+    a = UOp.range(3, -1)
+    out = x[a]*2
+    out = out.end(a)
+    out.realize()
+
+    self.assertTrue((out==2).all().item())
+
+  def test_vmap_outer(self):
+    x = Tensor.ones(3, 2).contiguous()
+
+    # vmap across axis 0
+    a = UOp.range(3, -1, AxisType.OUTER)
+    out = x[a]*2
+    out = out.end(a)
+    out.realize()
+
+    self.assertTrue((out==2).all().item())
+
+  def test_fancy_vmap(self):
+    def f(x,y): return x+y
+
+    x = Tensor.arange(9).reshape(3,3).contiguous()
+    y = Tensor.arange(9).reshape(3,3).contiguous()
+
+    a = UOp.range(3, -1)
+    out = f(x[:,a], y[a,:])
+    out = out.end(a).realize()
+    self.assertListEqual([[0,4,8],[4,8,12],[8,12,16]], out.tolist())
+
+  def test_vmap_inner_fusion(self):
+    x = Tensor.ones(3, 10, 2).contiguous()
+
+    # vmap across axis 0
+    a = UOp.range(3, -1)
+    out = x[a].sum(axis=0)*2
+    out = out.end(a)*4
+    out.realize()
+
+    self.assertTrue((out==10*2*4).all().item())
+
+  def test_vmap_outer_fusion(self):
+    x = Tensor.ones(3, 10, 2).contiguous()
+
+    # vmap across axis 0
+    a = UOp.range(3, -1, AxisType.OUTER)
+    out = x[a].sum(axis=0)*2
+    out = out.end(a)*4
+    out.realize()
+
+    self.assertTrue((out==10*2*4).all().item())
+
+  def test_vmap_outer_matmul(self):
+    x = Tensor.ones(1, 10).contiguous().requires_grad_()
+    mats = Tensor.ones(3, 10, 10).contiguous()
+
+    # vmap across axis 0
+    a = UOp.range(3, -1, AxisType.OUTER)
+    out = x @ mats[a]
+    out = out.end(a)
+
+    out.realize()
+
+  def test_vmap_outer_matmul_grad(self):
+    x = Tensor.ones(1, 10).contiguous().requires_grad_()
+    mats = Tensor.ones(3, 10, 10).contiguous().requires_grad_()
+
+    # vmap across axis 0
+    a = UOp.range(3, -1, AxisType.OUTER)
+    out = x @ mats[a]
+    out = out.end(a)
+    out.mean().backward()
+
+    mats.grad.realize()
+
 if __name__ == '__main__':
   unittest.main()

tinygrad/codegen/opt/postrange.py

@@ -52,8 +52,10 @@ class Scheduler:
   def get_optimized_ast(self, name_override:str|None=None):
     if name_override is not None: name = name_override
     else:
-      kernel_type = "r" if self.reduceop is not None else "E"
-      name = kernel_type + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), color) for x,color in zip(self.rngs, self.colors())])
+      k_type = "r" if self.reduceop is not None else "E"
+      special_uops = sorted([x for x in self.ast.toposort() if x.op is Ops.SPECIAL], key=lambda x: x.arg)
+      special_ops = [colored(str(x.vmax+1), "blue" if x.arg[0] == "g" else "cyan") for x in special_uops]
+      name = k_type + colored('_', 'BLACK').join(['']+special_ops+[colored(x.src[0].render(), color) for x,color in zip(self.rngs, self.colors())])
       Scheduler.kernel_cnt[(function_name := to_function_name(name))] += 1
       num = f"n{Scheduler.kernel_cnt[function_name]-1}" if Scheduler.kernel_cnt[function_name] > 1 else ""
       name += colored(num, 'BLACK')

tinygrad/engine/schedule.py

@@ -23,10 +23,12 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
   in_degree: dict[UOp, int] = {}
   var_vals: dict[str, int] = {}
   for u in sched_sink.toposort():
-    if u.op is not Ops.AFTER: continue  # anything that's not an ASSIGN doesn't write a kernel, so we can skip
+    if u.op is Ops.RANGE:
+      in_degree.setdefault(u, 0)
+      continue
+    if u.op is not Ops.AFTER or u.src[1].op is Ops.RANGE: continue
     k = u.src[1]
     in_degree.setdefault(k, 0)
-    if k.op is Ops.RANGE: continue
     for s in k.src[0].src if k.op is Ops.END else k.src:
       if s.op is Ops.AFTER:
         children[s.src[1]].append(k)
@@ -88,7 +90,7 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
       if rk.op is Ops.END: schedule.append(rk)
     else:
       raise RuntimeError(f"can't schedule {k.op}")
-    for x in children[k]:
+    for x in children[rk]:
       in_degree[x] -= 1
       if in_degree[x] == 0: queues[_heuristic(x)].append(x)
 

tinygrad/gradient.py

@@ -43,6 +43,8 @@ pm_gradient = PatternMatcher([
   (UPat(Ops.KERNEL, name="k"), lambda ctx, k: k.arg.grad_fxn(ctx, k)),
   # there's no gradient for bitcast
   (UPat(Ops.BITCAST), lambda: (None,)),
+  # this only works on single ends of outer ranges
+  (UPat(Ops.END, name="e"), lambda ctx, e: (ctx.shrink(((e.src[1],e.src[1]+1),)+(None,)*(len(ctx.shape)-1)).reshape(ctx.shape[1:]), None)),
 ])
 
 def _deepwalk(root:UOp, targets:set[UOp]) -> list[UOp]:

tinygrad/runtime/ops_amd.py

@@ -12,7 +12,7 @@ from tinygrad.renderer.cstyle import AMDRenderer
 from tinygrad.renderer.llvmir import AMDLLVMRenderer
 from tinygrad.runtime.autogen import kfd, hsa, pci, sqtt
 from tinygrad.runtime.autogen.am import am
-from tinygrad.runtime.support.compiler_amd import HIPCompiler, AMDLLVMCompiler
+from tinygrad.runtime.support.compiler_amd import HIPCompiler, HIPCCCompiler, AMDLLVMCompiler
 from tinygrad.runtime.support.elf import elf_loader
 from tinygrad.runtime.support.am.amdev import AMDev, AMMemoryManager
 from tinygrad.runtime.support.amd import AMDReg, AMDIP, import_module, import_soc, import_ip_offsets, import_pmc
@@ -909,7 +909,8 @@ class AMDDevice(HCQCompiled):
     self.sdma_queue = self.create_queue(kfd.KFD_IOC_QUEUE_TYPE_SDMA, 0x200 if self.is_usb() else (16 << 20))
 
     compilers:list[CompilerPairT] = [(functools.partial(AMDRenderer, self.arch), functools.partial(HIPCompiler, self.arch)),
-                                     (functools.partial(AMDLLVMRenderer, self.arch), functools.partial(AMDLLVMCompiler, self.arch))]
+                                     (functools.partial(AMDLLVMRenderer, self.arch), functools.partial(AMDLLVMCompiler, self.arch)),
+                                     (functools.partial(AMDRenderer, self.arch), functools.partial(HIPCCCompiler, self.arch))]
 
     super().__init__(device, AMDAllocator(self), compilers, functools.partial(AMDProgram, self), AMDSignal,
                      functools.partial(AMDComputeAQLQueue if self.is_aql else AMDComputeQueue, self),

tinygrad/runtime/support/compiler_amd.py

@@ -1,4 +1,4 @@
-import ctypes
+import ctypes, hashlib, tempfile, subprocess, pathlib
 from tinygrad.helpers import system
 from tinygrad.runtime.autogen import comgr
 try:
@@ -13,7 +13,7 @@ from tinygrad.runtime.support.compiler_cpu import LLVMCompiler
 from tinygrad.helpers import OSX, to_char_p_p
 
 def amdgpu_disassemble(lib:bytes):
-  asm = system(f"{'llvm-objdump' if OSX else '/opt/rocm/llvm/bin/llvm-objdump'} -d -", input=lib).splitlines()
+  asm = system(f"{'/opt/homebrew/opt/llvm/bin/llvm-objdump' if OSX else '/opt/rocm/llvm/bin/llvm-objdump'} -d -", input=lib).splitlines()
   while asm and ("s_nop 0" in asm[-1] or "s_code_end" in asm[-1]): asm.pop()
   print("\n".join(asm))
 
@@ -90,6 +90,24 @@ class HIPCompiler(Compiler):
     except RuntimeError as e: raise CompileError(e) from e
   def disassemble(self, lib:bytes): amdgpu_disassemble(lib)
 
+class HIPCCCompiler(Compiler):
+  def __init__(self, arch:str, extra_options:list[str]=[]):
+    self.arch, self.extra_options = arch, extra_options
+    super().__init__(f"compile_hipcc_{self.arch}_{hashlib.sha256(' '.join(extra_options).encode()).hexdigest()[:8]}")
+  def compile(self, src:str) -> bytes:
+    with tempfile.NamedTemporaryFile(suffix=".cpp") as srcf, tempfile.NamedTemporaryFile(suffix=".bc") as bcf:
+      with tempfile.NamedTemporaryFile(suffix=".hsaco") as libf:
+        srcf.write(src.encode())
+        srcf.flush()
+
+        subprocess.run(["hipcc", "-c", "-emit-llvm", "--cuda-device-only", "-O3", "-mcumode",
+                        f"--offload-arch={self.arch}", "-I/opt/rocm/include/hip", "-o", bcf.name, srcf.name] + self.extra_options, check=True)
+        subprocess.run(["hipcc", "-target", "amdgcn-amd-amdhsa", f"-mcpu={self.arch}",
+                        "-O3", "-mllvm", "-amdgpu-internalize-symbols", "-c", "-o", libf.name, bcf.name], check=True)
+
+        return pathlib.Path(libf.name).read_bytes()
+  def disassemble(self, lib:bytes): amdgpu_disassemble(lib)
+
 class AMDLLVMCompiler(LLVMCompiler):
   jit = False
   target_arch = "AMDGPU"

tinygrad/schedule/indexing.py

@@ -24,8 +24,8 @@ def realize_assign(ctx:dict[UOp, None], a:UOp) -> None:
 pm_generate_realize_map = PatternMatcher([
   # always realize SINK src
   (UPat(Ops.SINK, name="s"), lambda ctx,s: ctx.update((x.base, None) for x in s.src if x.base.op not in ALWAYS_CONTIGUOUS)),
-  # always realize COPY/BUFFER_VIEW/CONTIGUOUS/STORE
-  (UPat({Ops.COPY, Ops.BUFFER_VIEW, Ops.CONTIGUOUS, Ops.STORE}, name="tr"), realize),
+  # always realize COPY/BUFFER_VIEW/CONTIGUOUS/STORE/END
+  (UPat({Ops.COPY, Ops.BUFFER_VIEW, Ops.CONTIGUOUS, Ops.STORE, Ops.END}, name="tr"), realize),
   # realize srcs of COPY, MSELECT, MSTACK
   (UPat((Ops.COPY, Ops.MSELECT, Ops.MSTACK), name="rb"), realize_srcs),
   # realize ASSIGN and input to assign (might be optimized out)
@@ -66,6 +66,9 @@ def create_bufferize_and_index_based_on_ranges(ctx:IndexingContext, x:UOp):
         new_src = s.end(*[r for r in closed_ranges if r.op is Ops.RANGE])
         del ctx.realize_map[s]
       else:
+        if new_src.op is Ops.END:
+          # skip END
+          new_src = new_src.src[0]
         # None in the device assigns it a number later
         opts = BufferizeOpts(device=s.device) if len(ctx.range_map[s][1]) == len(realized_ranges) else BufferizeOpts(None, AddrSpace.LOCAL)
         new_src = UOp(Ops.BUFFERIZE, s.dtype, src=(new_src,)+closed_ranges, arg=opts, tag=s.tag if opts.addrspace == AddrSpace.GLOBAL else None)
@@ -173,8 +176,12 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
 
     consumer_rngs = [rctx.range_map[c][0] for c in consumer_map[x] if c in rctx.range_map]
     if x in rctx.realize_map:
-      # if this is in the realize_map, we create new ranges (at the output)
-      out_rngs = tuple(rctx.new_range(s) for s in x.shape)
+      if x.op is Ops.END:
+        # for END, we use the ranges in the src as the early ones
+        out_rngs = x.src[1:]+tuple(rctx.new_range(s) for s in x.src[0].shape)
+      else:
+        # if this is in the realize_map, we create new ranges (at the output)
+        out_rngs = tuple(rctx.new_range(s) for s in x.shape)
       # all ranges are ended now
       ending_ranges[x] = []
       # mark all ranges as ended
@@ -249,6 +256,10 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
     if x.op is Ops.REDUCE_AXIS:
       rngs = tuple(rctx.new_range(s, axistype=AxisType.REDUCE) if i in x.arg[1] else r for i,(r,s) in enumerate(zip(rngs, x.src[0].shape)))
 
+    # END ends ranges
+    if x.op is Ops.END:
+      rngs = rngs[len(x.src)-1:]
+
     if debug:
       realized_ranges = rctx.realize_map.get(x, None)
       if x.op is Ops.RESHAPE or len(rngs) != len(out_rngs):

tinygrad/schedule/rangeify.py

@@ -2,7 +2,7 @@ from dataclasses import dataclass, field
 import itertools
 from tinygrad.dtype import dtypes, PtrDType, ImageDType, AddrSpace
 from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, _substitute, ssimplify, KernelInfo
-from tinygrad.uop.ops import track_rewrites, graph_rewrite, identity_element, sint, AxisType, BottomUpGate, Kernel, _remove_all_tags
+from tinygrad.uop.ops import track_rewrites, graph_rewrite, identity_element, sint, AxisType, BottomUpGate, Kernel, _remove_all_tags, range_str
 from tinygrad.uop.symbolic import symbolic
 from tinygrad.helpers import argsort, prod, all_same, pluralize, getenv, flatten, dedup, all_int, DEBUG, SPLIT_REDUCEOP, DEBUG_RANGEIFY
 from tinygrad.helpers import PCONTIG, partition, get_single_element, unwrap, disable_gc
@@ -397,6 +397,9 @@ def handle_after(ctx:LocalAddBufferContext, after:UOp):
 
 def renumber_range(ctx:LocalAddBufferContext, r:UOp):
   if r.tag != (): return None
+  if r.arg[-1] == AxisType.OUTER:
+    # for outer range, we replace with a bound variable
+    return UOp.variable("range_"+range_str(r), r.vmin, r.vmax).bind(r.replace(tag=None))
   ret = r.replace(arg=(ctx.range,)+r.arg[1:], tag=None)
   ctx.range += 1
   return ret
@@ -469,6 +472,7 @@ pm_add_range_tags = PatternMatcher([
 ])
 
 def split_store(ctx:list[UOp], x:UOp) -> UOp|None:
+  # if we have any outer ranges open here, we don't split
   if len([r for r in x.ranges if r.arg[-1] != AxisType.OUTER]): return None
 
   # ends of outer range don't go in kernels
@@ -496,7 +500,12 @@ def split_store(ctx:list[UOp], x:UOp) -> UOp|None:
     raise RuntimeError(f"all buffers must be on the same device: {tuple(b.buf_uop.buffer for b in kernel.src)}")
   return kernel
 
+def split_inner_and_outer_end(x: UOp):
+  outer_ranges, inner_ranges = partition(x.src[1:], lambda r: r.arg[-1] == AxisType.OUTER)
+  if len(outer_ranges) and len(inner_ranges): return x.src[0].end(*inner_ranges).end(*outer_ranges)
+
 split_kernels = PatternMatcher([
+  (UPat(Ops.END, name="x"), split_inner_and_outer_end),
   (UPat((Ops.STORE, Ops.END), name="x"), split_store),
 ])
 
@@ -507,7 +516,7 @@ def tag_uop(ctx:list[UOp], x:UOp):
   return x.replace(tag=(len(ctx)-1,))
 add_tags = PatternMatcher([
   # don't tag BUFFERs, they are global
-  (UPat(GroupOp.All-{Ops.BUFFER, Ops.CONST, Ops.DEVICE, Ops.UNIQUE, Ops.DEFINE_VAR, Ops.BIND, Ops.KERNEL, Ops.END,
+  (UPat(GroupOp.All-{Ops.BUFFER, Ops.CONST, Ops.DEVICE, Ops.UNIQUE, Ops.DEFINE_VAR, Ops.BIND, Ops.KERNEL,
                      Ops.MSTACK, Ops.MSELECT, Ops.RANGE}.union(GroupOp.Movement), name="x"), tag_uop),
   (UPat({Ops.MSTACK, Ops.MSELECT}, name="x"), lambda ctx,x: None if all(s.op is Ops.BUFFER for s in x.src) else tag_uop(ctx, x)),
 ])
@@ -571,12 +580,12 @@ def get_rangeify_map(sink:UOp) -> dict[UOp, UOp]:
       assign_rep[a] = kernel_assign[s] = a.replace(src=a.src+(u,))
   if assign_rep: tsink = graph_rewrite(tsink, _substitute, ctx=assign_rep, bottom_up=True, name="fix_assign")
 
-  if getenv("VIZ"): graph_rewrite(tsink, PatternMatcher([]), name="View Kernel Graph")
-
   # TODO: we can probably get this earlier
   sink_tags = [s.tag for s in tsink.src]
   tsink = graph_rewrite(tsink, _remove_all_tags, name="remove all tags")
 
+  if getenv("VIZ"): graph_rewrite(tsink, PatternMatcher([]), name="View Kernel Graph")
+
   becomes_map: dict[UOp, UOp] = {}
   for tag, s in zip(sink_tags, tsink.src):
     assert tag is not None

tinygrad/tensor.py

@@ -481,6 +481,9 @@ class Tensor(OpMixin):
     if y.op is Ops.ADD: return Tensor.from_uop(y.src[0]) + Tensor.from_uop(y.src[1])
     raise RuntimeError(f"unhandled UOp {y}")
 
+  def end(self, *rngs:UOp):
+    return self._apply_uop(UOp.end, extra_args=rngs, dtype=self.dtype)
+
   # ***** creation entrypoint *****
 
   @staticmethod

tinygrad/uop/ops.py

@@ -219,9 +219,13 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
       case Ops.DEFINE_GLOBAL | Ops.DEFINE_LOCAL | Ops.DEFINE_REG: return (self.ptrdtype.size,)
 
       # passthrough ops
-      case Ops.REDUCE | Ops.MSTACK | Ops.MSELECT | Ops.DETACH | Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.AFTER | Ops.END:
+      case Ops.REDUCE | Ops.MSTACK | Ops.MSELECT | Ops.DETACH | Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.AFTER:
         return self.src[0]._shape
 
+      # end adds dims to the front
+      case Ops.END:
+        return None if self.src[0]._shape is None else (tuple(x.vmax+1 for x in self.src[1:]) + self.src[0]._shape)
+
       # ops with custom handling
       case Ops.KERNEL: return self.arg.ast._shape
 
@@ -398,9 +402,9 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
   def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, dtype=kwargs.pop("dtype", self.dtype.base), src=(self,)+src, **kwargs)
   def store(self, src:UOp|ConstType, **kwargs):
     return UOp(Ops.STORE, kwargs.pop("dtype", dtypes.void), (self, UOp.const(self.dtype, src) if not isinstance(src, UOp) else src), **kwargs)
-  def end(self, *src:UOp):
+  def end(self, *src:UOp, **kwargs):
     if len(src) == 0: return self
-    return UOp(Ops.END, src=(self,)+src)
+    return UOp(Ops.END, src=(self,)+src, **kwargs)
   def after(self, *src:UOp, **kwargs): return UOp(Ops.AFTER, self.dtype, (self,)+src, **kwargs)
   def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self, x))
   def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)
@@ -1265,15 +1269,12 @@ pm_lower_index_dtype = PatternMatcher([
   (UPat(Ops.SPECIAL, src=(UPat.var("var").cast(dtypes.index),), name="u"), lambda u,var: u.replace(dtype=dtypes.int, src=(var,)).cast(dtypes.index)),
   (UPat(Ops.DEFINE_VAR, dtype=dtypes.index, name="u"), lambda u: u.replace(dtype=dtypes.int).cast(dtypes.index)),
   (UPat(Ops.BIND, src=(UPat.var("var").cast(dtypes.index), UPat.cvar("val").cast(dtypes.index))), lambda var,val: var.bind(val).cast(dtypes.index)),
-  (UPat(Ops.CAST, src=(UPat(name="x").cast(dtypes.index),), name="c"), lambda x,c: x.cast(c.dtype)),
   # lower Invalid
   (UPat.var("buf").index(UPat.var("cond").where(UPat.var("idx"), UPat(Ops.CONST, arg=Invalid))), lambda buf,idx,cond: buf.index(idx, cond, ptr=True)),
   # remove hanging casts
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx", dtypes.ints).cast()),), lambda buf,idx: buf.index(idx, ptr=True)),
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx", dtypes.ints).cast(), UPat.var("valid"))),
    lambda buf,idx,valid: buf.index(idx, valid, ptr=True)),
-  (UPat((Ops.STORE, Ops.LOAD), src=(UPat(), UPat(), UPat().cast(dtypes.index)), allow_any_len=True, name="s"),
-    lambda s: s.replace(src=s.src[:2]+tuple(u.src[0] for u in s.src[2:]))),
   (UPat((Ops.SINK, Ops.NOOP, Ops.END), name="n"),
    lambda n: n.replace(src=tuple(s.src[0] if s.op is Ops.CAST and s.dtype == dtypes.index else s for s in n.src))),
 ])

tinygrad/uop/spec.py

@@ -42,7 +42,7 @@ shared_spec = PatternMatcher([
   (UPat(Ops.INDEX, src=(UPat(),), allow_any_len=True, name="x"), lambda x: all(y.dtype == dtypes.index for y in x.src[1:]) or None),
 
   # RANGE/SPECIAL define loops, END closes them
-  (UPat(Ops.END, src=(UPat(), UPat(Ops.RANGE)), dtype=dtypes.void), lambda: True),
+  (UPat(Ops.END, src=(UPat(), UPat(Ops.RANGE))), lambda: True),
 ])
 
 # ***** UOp spec in the Tensor graph *****
@@ -171,7 +171,7 @@ kernel_spec = PatternMatcher([
   (UPat(Ops.UNROLL, name="x"), lambda x: x.src[0].dtype.count == prod(y[1] for y in x.arg)),
 
   # END can end multiple axes here
-  (UPat(Ops.END, src=(UPat(), UPat()), allow_any_len=True, dtype=dtypes.void), lambda: True),
+  (UPat(Ops.END, src=(UPat(), UPat()), allow_any_len=True), lambda: True),
 
   # bufferize can be on anything
   (UPat(Ops.BUFFERIZE, src=(UPat(),), allow_any_len=True, name="x"), lambda x: True),