flip from_py to use weak dtypes [pr] (#17229)

test/null/test_dtype_spec.py

@@ -58,8 +58,8 @@ class TestHelpers(unittest.TestCase):
   def test_from_py(self):
     assert dtypes.from_py(True) == dtypes.bool
     assert dtypes.from_py(Invalid) == dtypes.bool
-    assert dtypes.from_py(2) == dtypes.default_int
-    assert dtypes.from_py(3.0) == dtypes.default_float
+    assert dtypes.from_py(2) == dtypes.weakint
+    assert dtypes.from_py(3.0) == dtypes.weakfloat
     assert dtypes.from_py([]) == dtypes.default_float
     assert dtypes.from_py(()) == dtypes.default_float
     assert dtypes.from_py([True]) == dtypes.bool
@@ -313,15 +313,15 @@ class TestAutoCastType(unittest.TestCase):
 
   @given(strat.sampled_from(core_dtypes))
   def test_broadcast_scalar(self, dt):
-    assert (Tensor.ones(4, 4, dtype=dt) + 2.3).dtype == (dt if dtypes.is_float(dt) else dtypes.default_float)
-    assert (Tensor.ones(4, 4, dtype=dt) + 2).dtype == (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.default_int)
+    assert (Tensor.ones(4, 4, dtype=dt) + 2.3).dtype == (dt if dtypes.is_float(dt) else dtypes.weakfloat)
+    assert (Tensor.ones(4, 4, dtype=dt) + 2).dtype == (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.weakint)
     assert (Tensor.ones(4, 4, dtype=dt) + True).dtype == dt
 
   @given(strat.sampled_from(core_dtypes))
   def test_pad_scalar(self, dt):
     t = Tensor.ones(4, dtype=dt)
-    assert t.pad(((1, 1),), value=2.3).dtype == (dt if dtypes.is_float(dt) else dtypes.default_float)
-    assert t.pad(((1, 1),), value=2).dtype == (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.default_int)
+    assert t.pad(((1, 1),), value=2.3).dtype == (dt if dtypes.is_float(dt) else dtypes.weakfloat)
+    assert t.pad(((1, 1),), value=2).dtype == (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.weakint)
     assert t.pad(((1, 1),), value=True).dtype == dt
 
   @given(strat.sampled_from(core_dtypes))
@@ -420,16 +420,16 @@ class TestAutoCastType(unittest.TestCase):
   @given(strat.sampled_from(core_dtypes))
   def test_where_one_scalar(self, dt):
     t = Tensor(2, dtype=dt)
-    self.check_where_alternate_input_other(t, 3.2, (dt if dtypes.is_float(dt) else dtypes.default_float))
-    self.check_where_alternate_input_other(t, 3, (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.default_int))
+    self.check_where_alternate_input_other(t, 3.2, (dt if dtypes.is_float(dt) else dtypes.weakfloat))
+    self.check_where_alternate_input_other(t, 3, (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.weakint))
     self.check_where_alternate_input_other(t, True, dt)
 
   def test_where_two_scalars(self):
-    self.check_where_alternate_input_other(3.1, 3.2, dtypes.default_float)
-    self.check_where_alternate_input_other(3.1, 3, dtypes.default_float)
-    self.check_where_alternate_input_other(3.1, True, dtypes.default_float)
-    self.check_where_alternate_input_other(3, 2, dtypes.default_int)
-    self.check_where_alternate_input_other(3, True, dtypes.default_int)
+    self.check_where_alternate_input_other(3.1, 3.2, dtypes.weakfloat)
+    self.check_where_alternate_input_other(3.1, 3, dtypes.weakfloat)
+    self.check_where_alternate_input_other(3.1, True, dtypes.weakfloat)
+    self.check_where_alternate_input_other(3, 2, dtypes.weakint)
+    self.check_where_alternate_input_other(3, True, dtypes.weakint)
 
   def test_where_non_bool_cond_raises(self):
     with self.assertRaises(RuntimeError): Tensor([1, 0, 2]).where(1, 0)
@@ -441,8 +441,8 @@ class TestAutoCastType(unittest.TestCase):
 
   @given(strat.sampled_from(core_dtypes))
   def test_maximum_const(self, dt):
-    assert Tensor([1, 2], dtype=dt).maximum(3.1).dtype == (dt if dtypes.is_float(dt) else dtypes.default_float)
-    assert Tensor([1, 2], dtype=dt).maximum(3).dtype == (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.default_int)
+    assert Tensor([1, 2], dtype=dt).maximum(3.1).dtype == (dt if dtypes.is_float(dt) else dtypes.weakfloat)
+    assert Tensor([1, 2], dtype=dt).maximum(3).dtype == (dt if dtypes.is_float(dt) or dtypes.is_int(dt) else dtypes.weakint)
     assert Tensor([1, 2], dtype=dt).maximum(True).dtype == dt
 
   def test_div(self):
@@ -453,7 +453,7 @@ class TestAutoCastType(unittest.TestCase):
 
   def test_div_const(self):
     assert (Tensor([1, 2], dtype=dtypes.int32) / 2).dtype == dtypes.default_float
-    assert (Tensor([1, 2], dtype=dtypes.int32) / 2.0).dtype == dtypes.default_float
+    assert (Tensor([1, 2], dtype=dtypes.int32) / 2.0).dtype == dtypes.weakfloat
     assert (Tensor([1, 2], dtype=dtypes.float16) / 2).dtype == dtypes.float16
     assert (Tensor([1, 2], dtype=dtypes.float16) / 2.0).dtype == dtypes.float16
 

test/null/test_tensor.py

@@ -181,7 +181,7 @@ class TestTensorPad(unittest.TestCase):
     t = Tensor.arange(9).reshape(1, 1, 3, 3)
     self.assertEqual(t.dtype, dtypes.int)
     r = t.pad((1, 2, 0, -1), value=-float('inf'))
-    self.assertEqual(r.dtype, dtypes.float)
+    self.assertEqual(r.dtype, dtypes.weakfloat)
     self.assertEqual(r.shape, (1, 1, 2, 6))
 
 class TestTensorDeviceMismatch(unittest.TestCase):

test/unit/test_dtype_spec.py

@@ -1,6 +1,6 @@
 import unittest, math, subprocess
 from tinygrad.tensor import Tensor
-from tinygrad.dtype import dtypes, DType, DTYPES_DICT
+from tinygrad.dtype import dtypes, DType, DTYPES_DICT, strong_dtype
 from tinygrad.device import Device
 from tinygrad.helpers import getenv, DEBUG, EMULATED_DTYPES
 from test.helpers import slow
@@ -25,6 +25,8 @@ def _assert_eq(tensor:Tensor, target_dtype:DType, target, tol_target_dtype:float
   if DEBUG >= 2: print(tensor.numpy())
   try:
     assert tensor.dtype == target_dtype
+    # weak values read back at their default.
+    target_dtype = strong_dtype(target_dtype)
     # denormals are zero
     if target_dtype in dtypes.floats and (target_dtype not in supported_dtypes or target_dtype in EMULATED_DTYPES.tolist(dtypes)):
       fe, fm = dtypes.finfo(target_dtype)
@@ -82,8 +84,8 @@ class TestTypeSpec(unittest.TestCase):
     dtypes.default_int, dtypes.default_float = default_int, default_float
     _assert_eq(Tensor(True), dtypes.bool, True)
     _assert_eq(Tensor(None), dtypes.default_float, [])
-    _assert_eq(Tensor(2), dtypes.default_int, 2)
-    _assert_eq(Tensor(2.34), dtypes.default_float, 2.34)
+    _assert_eq(Tensor(2), dtypes.weakint, 2)
+    _assert_eq(Tensor(2.34), dtypes.weakfloat, 2.34)
     _assert_eq(Tensor([]), dtypes.default_float, [])
     _assert_eq(Tensor([1]), dtypes.default_int, [1])
     # list elements are python scalars; a numpy scalar in a list has no inferred dtype (use np.array or state a dtype)

test/unit/test_dtype_weak.py

@@ -14,10 +14,11 @@ class TestWeakPromotion(unittest.TestCase):
     with self.assertRaises(ValueError): Tensor.const(dtypes.weakfloat, 1.0).rand_like()
     with self.assertRaises(ValueError): Tensor.const(dtypes.weakfloat, 1.0).randn_like()
 
-  def test_sum_stays_weak(self):
-    for weak, value in ((dtypes.weakfloat, 1.0),):
-      self.assertEqual(Tensor.const(weak, value).expand(3).sum().dtype, weak)
-    self.assertEqual((Tensor.const(dtypes.weakfloat, 1.0).expand(3).sum() + Tensor([1], dtype=dtypes.float16)).dtype, dtypes.float16)
+  def test_reduce_strips_weakness(self):
+    for weak, value, strong in ((dtypes.weakint, 1, dtypes.default_int), (dtypes.weakfloat, 1.0, dtypes.default_float)):
+      t = Tensor.const(weak, value).expand(3)
+      for out in (t.sum(), t.max(), t.prod(), t.cumsum(0), t.cummax(0)[0]): self.assertEqual(out.dtype, strong)
+    self.assertEqual((Tensor.const(dtypes.weakfloat, 1.0).expand(3).sum() + Tensor([1], dtype=dtypes.float16)).dtype, dtypes.float32)
 
   def test_materialize_at_default_dtype(self):
     for weak, value, strong in ((dtypes.weakfloat, 0.5, dtypes.default_float),):
@@ -67,7 +68,10 @@ class TestWeakPromotion(unittest.TestCase):
     self.assertEqual((t_f32 + t_f16).dtype, dtypes.float32)
     self.assertEqual(Tensor([2], dtype=dtypes.uint8).pad(((1, 1),), value=1).dtype, dtypes.uint8)
 
-  @unittest.expectedFailure  # TODO: dot of a weak const tensor defers to the other operand once python scalars are weak consts
+  def test_concrete_pair_promotes_weak(self):
+    out = Tensor([-1], dtype=dtypes.int64, device="CPU") + Tensor([3], dtype=dtypes.uint64, device="CPU") + Tensor(0.5)
+    self.assertEqual((out.dtype, out.tolist()), (dtypes.weakfloat, [2.5]))
+
   def test_dot_defers_weak(self):
     weak = Tensor([True, False]).where(Tensor(1), 2)
     self.assertEqual(weak.dot(Tensor([1, 1], dtype=dtypes.int8)).dtype, dtypes.int8)
@@ -102,7 +106,6 @@ class TestWeakPromotion(unittest.TestCase):
     x32 = Tensor.full((1,), 0.0, dtype=dtypes.float32, device="CPU")
     self.assertEqual((x32 + value).item(), 1.0)
 
-  @unittest.expectedFailure  # TODO: exp/cos/sigmoid of a weak const stay weak instead of casting to a concrete float
   def test_weak_transcendentals(self):
     t_f16 = Tensor([1], dtype=dtypes.float16)
     for out in (Tensor(2).exp(), Tensor(2).cos(), Tensor(2).sigmoid()):

tinygrad/codegen/decomp/dtype.py

@@ -24,7 +24,10 @@ def l2i(op: Ops, dt: DType, *uops:UOp):
   match op:
     case Ops.NEG: return l2i(Ops.SUB, dt, zero, zero, *uops)
     case Ops.CAST if dt in (dtypes.long, dtypes.ulong) and uops[0].dtype not in dtypes.floats:
-      return uops[0].cast(l2i_dt[dt]), (uops[0] < 0).where(UOp.const(l2i_dt[dt], -1), UOp.const(l2i_dt[dt], 0))
+      # the high word is the sign extension; bool has no sign, test the already-cast low word instead (bool < 0 would promote to weakint)
+      x, lo = uops[0], uops[0].cast(l2i_dt[dt])
+      sign = lo if x.dtype is dtypes.bool else x
+      return lo, (sign < sign.const_like(0)).where(lo.const_like(-1), lo.const_like(0))
     case Ops.CAST if dt in (dtypes.long, dtypes.ulong):
       return (lo:=uops[0].cast(l2i_dt[dt])), (uops[0] / 2**32).cast(l2i_dt[dt]) - ((uops[0] < 0) & lo.ne(0)).cast(l2i_dt[dt])
     case Ops.CAST if dt in dtypes.floats:

tinygrad/dtype.py

@@ -99,8 +99,8 @@ class dtypes:
   def from_py(x) -> DType:
     # NOTE: isinstance(True, int) is True, so bool must be checked before int
     if isinstance(x, (bool, InvalidType)): return dtypes.bool
-    if isinstance(x, float): return dtypes.default_float
-    if isinstance(x, int): return dtypes.default_int
+    if isinstance(x, float): return dtypes.weakfloat
+    if isinstance(x, int): return dtypes.weakint
     # put this in the last is faster because there are more items than lists/tuples to check
     if isinstance(x, (list, tuple)): return strong_dtype(max(dtypes.from_py(xi) for xi in x)) if x else dtypes.default_float
     raise RuntimeError(f"Could not infer dtype of {x} with type {type(x)}")
@@ -208,7 +208,6 @@ def can_lossless_cast(dt0:DType, dt1:DType) -> bool:
 
 def sum_acc_dtype(dt:DType):
   # default acc dtype for sum
-  if dt in dtypes.weaks: return dt
   if dtypes.is_unsigned(dt): return least_upper_dtype(dt, dtypes.uint)
   if dtypes.is_int(dt) or dt == dtypes.bool: return least_upper_dtype(dt, dtypes.int)
   return least_upper_dtype(dt, to_dtype(getenv("SUM_DTYPE", "float32")))
@@ -303,6 +302,7 @@ def _from_np_dtype(npdtype:'np.dtype') -> DType: # type: ignore [name-defined] #
 @functools.cache
 def _to_torch_dtype(dtype:DType) -> 'torch.dtype'|None:  # type: ignore [name-defined] # noqa: F821
   import numpy as np, torch
+  dtype = strong_dtype(dtype)
   if dtype == dtypes.uint64: return torch.uint64
   if dtype == dtypes.bfloat16: return torch.bfloat16
   if dtype in dtypes.fp8s: return torch.uint8

tinygrad/mixin/reduce.py

@@ -1,6 +1,6 @@
 from typing import Self, Sequence
 from tinygrad.uop import Ops
-from tinygrad.dtype import DTypeLike, dtypes, sum_acc_dtype, to_dtype
+from tinygrad.dtype import DTypeLike, dtypes, strong_dtype, sum_acc_dtype, to_dtype
 from tinygrad.helpers import make_tuple
 from tinygrad.mixin.dtype import DTypeMixin
 from tinygrad.mixin.movement import MovementMixin
@@ -11,6 +11,7 @@ class ReduceMixin(DTypeMixin, MovementMixin):
     raise NotImplementedError
 
   def _reduce(self, op:Ops, axis:int|Sequence[int]|None=None, keepdim=False) -> Self:
+    if self.dtype in dtypes.weaks: self = self.cast(strong_dtype(self.dtype))
     axis = tuple(self._resolve_dim(x) for x in (range(self.ndim) if axis is None else make_tuple(axis, 1)))
     if self.ndim == 0: axis = ()
     ret = self._rop(op, axis)

tinygrad/renderer/isa/x86.py

@@ -193,8 +193,9 @@ pre_isel_matcher = PatternMatcher([
   (UPat((Ops.INDEX, Ops.SHRINK), name="addr").store(UPat.var("val"), UPat.var("gate")), gated_store),
   # TODO: remove this once we allow all flag producing ops in cmove
   # if gate in scalar int cmove is not a comparison need to add one to set the flag
+  # NOTE: the 0 is int so the bool gate zero-extends and compares as int (a byte compare renders different kernels)
   (UPat.var("m", dtypes.bool).where(UPat.var("a"), UPat.var("b")),
-   lambda m,a,b: m.ne(0).where(a,b) if m.op not in GroupOp.Comparison else None),
+   lambda m,a,b: m.ne(UOp.const(dtypes.int, 0)).where(a,b) if m.op not in GroupOp.Comparison else None),
 ])
 
 # ***** X86 registers *****

tinygrad/renderer/wgsl.py

@@ -13,6 +13,8 @@ def sign_extend(val:UOp, sext_am:int):
 def packed_store(bidx:UOp, var:UOp, gate:UOp|None=None):
   elems, mask = 4//var.dtype.itemsize, _mask(var.dtype)
   shift_am, div_idx = (bidx.src[1].cast(dtypes.uint32) % elems) * (8*var.dtype.itemsize), bidx.src[1] // elems
+  # bool does its mask math at int32: renderer rewrites run after weak dtypes are lowered, and bool & 0xFF would create a weakint const
+  if var.dtype == dtypes.bool: var = var.cast(dtypes.int32)
   new_v, wmask = (var & mask).cast(dtypes.uint32) << shift_am, ((mask << shift_am) ^ 0xFFFFFFFF).cast(dtypes.uint32)
   idx = UOp(Ops.INDEX, src=(bidx.src[0], div_idx))
   buf = UOp.load(idx, *((UOp.const(dtypes.uint32, 0), gate) if gate is not None else ()), dtype=dtypes.uint32)

tinygrad/schedule/rangeify.py

@@ -1,7 +1,7 @@
 from dataclasses import dataclass, field, replace
 from typing import cast
 import itertools
-from tinygrad.dtype import dtypes, AddrSpace, Invalid, to_dtype
+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, profile_matches, identity_element
 from tinygrad.uop.symbolic import symbolic
@@ -359,6 +359,7 @@ pm_limit_bufs = PatternMatcher([(UPat(set.union(GroupOp.Binary, GroupOp.Ternary)
 
 def bufferize_to_store(ctx:itertools.count, x:UOp, idx:UOp, allow_locals=True):
   size = prod(x.shape)
+  dtype = strong_dtype(x.dtype)  # a BUFFER is never weak: store at the concrete dtype, the .cast(x.dtype) on the result keeps readers unchanged
   rngs = sorted(idx.ranges, key=lambda x: x.arg)
   assert size > 0 and isinstance(size, int), f"no zero sized or symbolic sized buffers {size}"
 
@@ -379,15 +380,15 @@ def bufferize_to_store(ctx:itertools.count, x:UOp, idx:UOp, allow_locals=True):
 
   # NOTE: the local BUFFER needs to be disambiguated here
   if x.arg.addrspace == AddrSpace.GLOBAL:
-    buf = UOp(Ops.BUFFER, src=(shape_to_shape_arg((size,)),), arg=ParamArg(next(ctx), x.dtype, device=x.arg.device, addrspace=AddrSpace.GLOBAL))
-    do_store = buf.index(idx).store(x.src[0]).end(*rngs)
-    return buf.after(do_store)
+    buf = UOp(Ops.BUFFER, src=(shape_to_shape_arg((size,)),), arg=ParamArg(next(ctx), dtype, device=x.arg.device, addrspace=AddrSpace.GLOBAL))
+    do_store = buf.index(idx).store(x.src[0].cast(dtype)).end(*rngs)
+    return buf.after(do_store).cast(x.dtype)
 
   if allow_locals:
     # handle locals
-    buf = UOp.placeholder((size,), x.dtype, next(ctx), AddrSpace.LOCAL)
-    do_store = buf.index(idx).store(x.src[0]).end(*rngs)
-    return buf.after(do_store.barrier())
+    buf = UOp.placeholder((size,), dtype, next(ctx), AddrSpace.LOCAL)
+    do_store = buf.index(idx).store(x.src[0].cast(dtype)).end(*rngs)
+    return buf.after(do_store.barrier()).cast(x.dtype)
 
 # collapse any BUFFERIZE to single input BUFFERIZE
 def flatten_bufferize(x:UOp):
@@ -412,6 +413,11 @@ def remove_noop_afters(x:UOp) -> UOp|None:
 pm_add_buffers = pm_mops+pm_flatten_bufferize+PatternMatcher([
   (UPat(Ops.STAGE, src=(UPat(), UPat(name="idx")), name="x"), lambda ctx,x,idx: bufferize_to_store(ctx, x, idx, allow_locals=False)),
 
+  # INDEX of a buffer through the weak cast added above: index the buffer directly and cast the loaded value instead.
+  # this must run in the same rewrite that adds the cast, or the expander expands the whole casted buffer into one big VECTORIZE
+  (UPat(Ops.INDEX, src=(UPat(Ops.CAST, dtype=dtypes.weaks, src=(UPat.var("buf"),)),), allow_any_len=True, name="u"),
+   lambda u,buf: u.replace(dtype=None, src=(buf,)+u.src[1:]).cast(u.dtype)),
+
   # move RESHAPEs through MSELECT/MSTACK
   (UPat((Ops.MSELECT, Ops.MSTACK), src=UPat(Ops.RESHAPE), name="m"),
    lambda m: m.replace(src=tuple([x.src[0].base for x in m.src])).reshape(m.shape)),

tinygrad/uop/ops.py

@@ -1728,6 +1728,10 @@ def lower_weak_node(u:UOp) -> UOp|None:
   return u.replace(dtype=None, src=src[:start]+tuple(s.cast(dt) for s in src[start:])).cast(u.dtype)
 pm_lower_weak = PatternMatcher([
   (UPat(Ops.CONST, dtype=dtypes.weaks, name="u"), lambda u: u.replace(dtype=select_dtype(u)).cast(u.dtype)),
+  # two stacked weak casts are a weakint value used as weakfloat (or vice versa): resolve the inner one at the outer kind's default.
+  # a SINGLE weak cast is never rewritten here, each consumer absorbs it on its own edge (see lower_weak_srcs)
+  (UPat(Ops.CAST, dtype=dtypes.weaks, src=(UPat(Ops.CAST, dtype=dtypes.weaks, src=(UPat.var("x"),)),), name="u"),
+   lambda u,x: x.cast(select_dtype(u)).cast(u.dtype) if x.dtype not in dtypes.weaks else None),
   # Binary can widen from the bounds, all other nodes derive from the lowered sources.
   # a weakfloat Unary (sin/exp2/...) must resolve here, before the transcendental decomposition
   (UPat(GroupOp.Binary|GroupOp.Unary|{Ops.WHERE, Ops.RANGE, Ops.STACK}, name="u"), lower_weak_node),