variables should not be weakints

examples/beautiful_mnist.py

@@ -1,6 +1,6 @@
 # model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
 from typing import Callable
-from tinygrad import Tensor, TinyJit, nn, GlobalCounters, Context
+from tinygrad import Tensor, TinyJit, nn, GlobalCounters, function, Context
 from tinygrad.helpers import getenv, colored, trange
 from tinygrad.nn.datasets import mnist
 
@@ -15,6 +15,7 @@ class Model:
       nn.BatchNorm(64), Tensor.max_pool2d,
       lambda x: x.flatten(1), nn.Linear(576, 10)]
 
+  @function
   def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
 
   @TinyJit

tinygrad/schedule/__init__.py

@@ -80,8 +80,7 @@ def create_schedule(sched_sink:UOp) -> UOp:
 
 from tinygrad.schedule.memory import memory_plan_rewrite
 from tinygrad.engine.realize import capturing, pm_flatten_linear
-#from tinygrad.schedule.rangeify import get_kernel_graph
-from tinygrad.schedule.rangeify2 import get_kernel_graph
+from tinygrad.schedule.rangeify import get_kernel_graph
 from tinygrad.helpers import CAPTURING
 from tinygrad.uop.ops import PatternMatcher, UPat, ParamArg
 from tinygrad.dtype import AddrSpace

tinygrad/schedule/rangeify2.py

@@ -1,238 +0,0 @@
-from dataclasses import dataclass, field, replace
-from typing import cast
-import itertools
-from tinygrad.dtype import dtypes, AddrSpace, Invalid, to_dtype, strong_dtype
-from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, KernelInfo, ParamArg, shape_to_shape_arg
-from tinygrad.uop.ops import graph_rewrite, sint, AxisType, BottomUpGate, rewrite_group, identity_element, remove_all_tags
-from tinygrad.uop.symbolic import symbolic, pm_fold_cast_const
-from tinygrad.uop.movement import mop_cleanup
-from tinygrad.helpers import prod, getenv, dedup, all_int, DEBUG, SPLIT_REDUCEOP, DEBUG_RANGEIFY, VIZ, MAX_KERNEL_BUFFERS, SPEC
-from tinygrad.helpers import PCONTIG, FLOAT16, OPENPILOT_HACKS, argsort, partition, get_single_element, Context
-from tinygrad.codegen.simplify import pm_flatten_range, pm_reduce_simplify
-from tinygrad.codegen.opt import Opt
-from tinygrad.schedule.indexing import run_rangeify, BufferizeOpts, IndexingContext, apply_movement_op
-from tinygrad.schedule.multi import multi_pm
-from tinygrad.schedule.allreduce import create_allreduce_function
-
-# *** preparation ***
-
-from tinygrad.helpers import all_same
-from tinygrad.uop.ops import _broadcast_shape
-
-def expand_broadcast(x:UOp):
-  shapes = [u._shape for u in x.src]
-  if any(s is None for s in shapes) or all_same(shapes): return None
-  shape = _broadcast_shape(*shapes)
-  return x.replace(src=tuple([u.expand(shape) for u in x.src]))
-
-pm_expand_broadcast = PatternMatcher([
-  # expand broadcasts first
-  (UPat(GroupOp.Binary|GroupOp.Ternary|{Ops.STORE}, name="x"), expand_broadcast),
-])
-
-def convert_copy_to_store(ctx, copy:UOp, existing_buf:UOp|None=None):
-  input_src = copy.src[0]
-  if not input_src.has_buffer_identity(after_ok=True): input_src = input_src.contiguous()
-  input_src = input_src.flatten()
-  if existing_buf is not None:
-    # if the existing buffer is not a full buffer, we can't use it
-    if not existing_buf.has_buffer_identity(after_ok=True): return None
-    # if there's already a buffer, we just use it
-    return existing_buf.flatten().store(input_src)
-  # create the output buffer
-  buf = UOp(Ops.BUFFER, src=(shape_to_shape_arg(input_src.max_shape),), arg=ParamArg(next(ctx), copy.dtype, device=copy.device))
-  # reshape back to input
-  return buf.after(buf.store(input_src)).reshape(copy.shape)
-
-def convert_contig_to_store(ctx, copy:UOp):
-  input_src = copy.src[0]
-  # create the output buffer
-  buf = UOp(Ops.BUFFER, src=(shape_to_shape_arg(input_src.max_shape),), arg=ParamArg(next(ctx), copy.dtype, device=copy.device))
-  # reshape back to input
-  view = buf.shrink_to(input_src.shape)
-  return view.after(view.store(input_src))
-
-pm_copy_to_store = PatternMatcher([
-  (UPat(name="existing_buf").store(UPat(Ops.COPY, name="copy")), convert_copy_to_store),
-  (UPat(Ops.COPY, name="copy"), convert_copy_to_store),
-  (UPat(Ops.CONTIGUOUS, name="copy"), convert_contig_to_store),
-])
-
-# *** RANGE creation ***
-
-def rangeify_on_reduce(ctx, inp:UOp, red:UOp, idx:UOp|None=None):
-  if red.arg[1] == 0: return None
-  if idx is None and len(red.shape) > 0: return None
-  # TODO: is AxisType.REDUCE a real thing?
-  rngs = [UOp.range(s, next(ctx), AxisType.REDUCE) for s in inp.shape[:red.arg[1]]]
-  return inp.index(*rngs, *(idx.src[1:] if idx is not None else ())).reduce(*rngs, arg=(red.arg[0], 0))
-
-def rangeify_on_store(ctx, x:UOp):
-  if x.shape == (): return None
-  rngs = [UOp.range(s, next(ctx)) for s in x.shape]
-  return x.src[0].index(*rngs).store(x.src[1].index(*rngs)).end(*rngs)
-
-def rangeify_on_stage(ctx, x:UOp):
-  if x.src[0].shape == (): return None
-  # size 1 dims don't get ranges, they are reshaped out and back in
-  if all_int(x.shape) and 0 < len(sq := tuple(s for s in x.shape if s != 1)) < len(x.shape):
-    return rangeify_on_stage(ctx, x.src[0].reshape(sq).bufferize(arg=x.arg)).reshape(x.shape)
-  rngs = [UOp.range(s, next(ctx)) for s in x.shape]
-  return x.replace(src=(x.src[0].index(*rngs), *rngs))
-
-pm_range_creation = PatternMatcher([
-  # reduce/store are what creates ranges
-  (UPat(Ops.REDUCE, src=(UPat.var('inp'),), name="red").index(name="idx", allow_any_len=True), rangeify_on_reduce),
-  (UPat(Ops.REDUCE, src=(UPat.var('inp'),), name="red"), rangeify_on_reduce),
-  (UPat(Ops.STORE, name="x"), rangeify_on_store),
-  (UPat(Ops.STAGE, name="x"), rangeify_on_stage),
-])
-
-# *** RANGE migration ***
-
-# movement op on INDEX as a PatternMatcher
-def _mop_index(r:UOp, idx:UOp):
-  idxs = idx.src[1:]
-  if len(idxs) == len(r.shape):
-    ret = r.src[0].index(*apply_movement_op(r.op, r.src[0].shape, r.marg, idxs), dtype=idx.dtype, arg=idx.arg)
-    if r.op is Ops.PAD:
-      # insert 0 for PAD with where
-      # TODO: does this need simplify to ensure the Invalids are at the base?
-      a = UOp.const(True)
-      for s in ret.src[1:]:
-        if s.op is Ops.WHERE and s.src[2].op is Ops.CONST and s.src[2].arg == Invalid: a = a & s.src[0]
-      ret = a.where(ret, ret.const_like(0))
-    return ret
-  if r.op is Ops.RESHAPE:
-    src_prefix = len(r.src[0].shape) - len(r.shape[len(idxs):])
-    if src_prefix >= 0 and r.src[0].shape[src_prefix:] == r.shape[len(idxs):]:
-      if src_prefix == 0: return r.src[0] if r.src[0].dtype == idx.dtype else None
-      ret = r.src[0].index(*apply_movement_op(r.op, r.src[0].shape[:src_prefix], r.shape[:len(idxs)], idxs), dtype=idx.dtype, arg=idx.arg)
-      return ret if ret.shape == idx.shape else None
-
-# TODO: this should be in _mop_index
-def index_on_stack(stack:UOp, idx:UOp):
-  srcs = [s.index(*idx.src[2:]) for s in stack.src]
-  r0 = idx.src[1]
-  ret = srcs[-1]
-  for k in range(len(srcs)-2, -1, -1): ret = r0.eq(k).where(srcs[k], ret)
-  return ret
-
-def walk_mop(u:UOp):
-  if u.op in GroupOp.Movement or u.op is Ops.INDEX: return u.src[0]
-  assert u.op == Ops.AFTER
-  return u
-
-pm_range_migration = PatternMatcher([
-  # INDEX without src is nothing
-  (UPat(Ops.INDEX, src=(UPat.var('x'),)), lambda x: x),
-  # STAGE on shape () is nothing
-  (UPat(Ops.STAGE, src=(UPat.var('x'),)), lambda x: x if x.shape == () else None),
-  # if INDEX is on STAGE with the same ranges, remove the pair
-  (UPat(Ops.STAGE, allow_any_len=True, name="s").index(allow_any_len=True, name="i"),
-   lambda s,i: s.src[0] if s.src[1:] == i.src[1:] else None),
-  # reshape of a single element shaped value to scalar is an index
-  (UPat(Ops.RESHAPE, name="x"), lambda x: x.src[0].index(0) if x.marg == () and x.src[0].shape == (1,) else None),
-  # handle movement ops on INDEX
-  (UPat(GroupOp.Movement, name="r").index(name="idx", allow_any_len=True), _mop_index),
-  (UPat(Ops.STACK, name="stack").index(name="idx", allow_any_len=True), index_on_stack),
-  # move movement ops and INDEX after AFTER
-  (UPat(GroupOp.Movement|{Ops.INDEX}, name="r").after(name="a", allow_any_len=True),
-   lambda r,a: UOp(r.op, src=(a.replace(src=(r.src[0],)+a.src[1:]),)+r.src[1:], arg=r.arg)),
-  # pass index through elementwise
-  (UPat(GroupOp.Elementwise, name="b").index(name="idx", allow_any_len=True),
-   lambda b,idx: b.replace(src=tuple(s.index(*idx.src[1:]) for s in b.src))),
-  # remove movement ops from SINK. TODO: should be generic
-  (UPat(Ops.SINK, name="s"), lambda s: s.replace(src=tuple(walk_mop(u) for u in s.src))),
-])
-
-# *** split into kernels ***
-
-@dataclass
-class SplitCtx:
-  call_args:list = field(default_factory=list)
-  range_number:int = -1
-
-def _split_graph(ctx:SplitCtx, u:UOp) -> UOp:
-  assert len(u.shape) <= 1, f"rangeify needs to reduce to a single idx, not {u.shape}"
-  ctx.call_args.append(u)
-  return u.param_like(len(ctx.call_args)-1)
-
-def _renumber_range(ctx:SplitCtx, u:UOp) -> UOp:
-  ctx.range_number += 1
-  return u.replace(arg=(ctx.range_number, u.arg[-1]))
-
-pm_split_graph = PatternMatcher([
-  (UPat((Ops.PARAM, Ops.AFTER, Ops.BUFFER), name="u"), _split_graph),
-  (UPat(Ops.RANGE, name="u"), _renumber_range),
-])
-
-def split_store(x:UOp) -> UOp:
-  ret = graph_rewrite(x, pm_split_graph, ctx:=SplitCtx(), name="split kernel", bottom_up=True, walk=True)
-  return ret.sink(arg=KernelInfo()).call(*ctx.call_args)
-
-split_kernels = PatternMatcher([
-  (UPat((Ops.STORE, Ops.END), name="x"), split_store),
-])
-
-# *** main rangeify ***
-
-debug_tag_factor = PatternMatcher([
-  (UPat(GroupOp.All, name="x"), lambda ctx,x: x.rtag(ctx[0][x] if x not in ctx[1] else 'REAL') if x.tag is None else None),
-])
-
-def remove_stage(ctx, x:UOp) -> UOp:
-  buf = UOp.new_buffer(x.arg.device, x.max_numel(), x.dtype, num=next(ctx))
-  return buf.after(buf.reshape(x.shape).index(*x.src[1:]).store(x.src[0]).end(*x.src[1:])).reshape(x.shape)
-pm_remove_stage = PatternMatcher([(UPat(Ops.STAGE, name="x"), remove_stage)])
-
-@rewrite_group(new_ctx=False)
-def get_kernel_graph(sink:UOp) -> UOp:
-  # TODO: multi should just be part of rangeify
-  tsink = graph_rewrite(sink, multi_pm, name="multi_pm")
-
-  # prepare
-  tsink = graph_rewrite(tsink, pm_expand_broadcast, bottom_up=True, name="expand broadcast")
-  tsink = graph_rewrite(tsink, pm_copy_to_store, ctx=itertools.count(0), bottom_up=True, name="convert copy to store")
-
-  # add safe STAGEs to never duplicate compute
-  # we compute the number of times a buffer is consumed. if > 1, we realize
-  realize = {}
-  consumes = {tsink:0}
-  for u in reversed(tsink.toposort()):
-    assert u in consumes, f"{u.op} not in consumes"
-    if (u.op in GroupOp.ALU or u.op is Ops.REDUCE) and consumes[u] > 1 and u.device is not None:
-      # TODO: rename to stage
-      realize[u] = u.rtag(1).bufferize(arg=BufferizeOpts(device=u.device))
-      consumes[u] = 1
-    if u.op is Ops.STORE: consumes[u] = 1
-    if u.op is Ops.EXPAND: consumes[u] *= u.max_numel() // u.src[0].max_numel()
-    for i,s in enumerate(u.src):
-      if s not in consumes: consumes[s] = 0
-      if u.op is not Ops.STORE or i > 0:
-        consumes[s] += consumes[u]
-  if VIZ:
-    with Context(TRACK_MATCH_STATS=0): ctags = graph_rewrite(tsink, debug_tag_factor, ctx=(consumes, realize), bottom_up=True)
-    graph_rewrite(ctags, PatternMatcher([]), name="View Consumes")
-
-  # add stages
-  tsink = graph_rewrite(tsink.substitute(realize), remove_all_tags, name="untag")
-
-  # simple rangeify
-  tsink = graph_rewrite(tsink, pm_range_creation+pm_range_migration, ctx=itertools.count(0), bottom_up=True, name="simple rangeify")
-
-  # TODO: merging and splitting algorithm
-
-  if VIZ: graph_rewrite(tsink, PatternMatcher([]), name="View Rangeify")
-
-  tsink = graph_rewrite(tsink, pm_remove_stage, ctx=itertools.count(0), bottom_up=True, name="remove stage")
-
-  tsink = graph_rewrite(tsink, split_kernels, bottom_up=True, name="split kernels")
-
-  if VIZ: graph_rewrite(tsink, PatternMatcher([]), name="View Kernel Graph")
-  if SPEC:
-    # validate the kernel graph
-    from tinygrad.uop.spec import type_verify, spec_kernel_graph
-    type_verify(tsink, spec_kernel_graph, enter_calls=False)
-  return tsink
-

tinygrad/uop/ops.py

@@ -969,7 +969,7 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
   # *** uop Variable stuff ***
 
   @staticmethod
-  def variable(name:str, min_val:PyConst, max_val:PyConst, dtype:DType=dtypes.weakint, multiple_of:int=1, param:bool=False) -> UOp:
+  def variable(name:str, min_val:PyConst, max_val:PyConst, dtype:DType=dtypes.int, multiple_of:int=1, param:bool=False) -> UOp:
     # a Variable is a 0-d BUFFER in the ALU addrspace; binding it is storing a CONST into it
     # param=True creates the kernel-side form directly: an ALU PARAM (what the BUFFER becomes inside kernels)
     arg = ParamArg(-1, dtype, name=name, vmin_vmax=(min_val, max_val), multiple_of=multiple_of, addrspace=AddrSpace.ALU)