no function in bmnist

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, function, Context
+from tinygrad import Tensor, TinyJit, nn, GlobalCounters, Context
 from tinygrad.helpers import getenv, colored, trange
 from tinygrad.nn.datasets import mnist
 
@@ -15,7 +15,6 @@ 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

@@ -81,7 +81,8 @@ 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.rangeify import get_kernel_graph
+from tinygrad.schedule.rangeify2 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

@@ -0,0 +1,238 @@
+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
+