confirm it works in amd_uop_matmul

.github/workflows/test.yml

@@ -243,9 +243,8 @@ jobs:
       run: |
         python -m mypy --strict-equality --lineprecision-report .
         cat lineprecision.txt
-    # broken because of UPatAny
-    #- name: Run TYPED=1
-    #  run: TYPED=1 python -c "import tinygrad"
+    - name: Run TYPED=1
+      run: TYPED=1 python -c "import tinygrad"
 
   unittest:
     name: Unit Tests

extra/gemm/torch_gemm.py

@@ -9,7 +9,6 @@ torch.set_num_threads(1)
 from tinygrad.helpers import getenv
 CUDA = getenv("CUDA", 1)
 MPS = getenv("MPS", 0)
-if getenv("FP16_ACC"): torch.backends.cuda.matmul.allow_fp16_accumulation = True
 
 for dtype in [torch.float32, torch.float16, torch.bfloat16]:
   for N in [256, 512, 1024, 2048, 4096]:

setup.py

@@ -52,7 +52,7 @@ setup(name='tinygrad',
         "License :: OSI Approved :: MIT License"
       ],
       install_requires=[],
-      python_requires='>=3.11',
+      python_requires='>=3.10',
       extras_require={
         'arm': ["unicorn"],
         'triton': ["triton-nightly>=2.1.0.dev20231014192330"],

tinygrad/tensor.py

@@ -9,7 +9,7 @@ from tinygrad.helpers import argfix, make_tuple, flatten, prod, all_int, round_u
 from tinygrad.helpers import IMAGE, WINO, Metadata, TRACEMETA, ceildiv, fetch, polyN, DEBUG, is_numpy_ndarray, FUSE_ATTENTION, SPEC
 from tinygrad.helpers import suppress_finalizing
 from tinygrad.gradient import compute_gradient
-from tinygrad.uop.mixins import MathMixin, MovementMixin
+from tinygrad.uop.mathtraits import MathTrait
 from tinygrad.uop.ops import smax, smin, resolve, UOp, Ops, sint, identity_element, all_metadata, _index_to_concrete_int, sint_to_uop
 from tinygrad.uop.spec import type_verify, tensor_spec
 from tinygrad.device import Device, Buffer
@@ -100,7 +100,7 @@ def _flat_to_grouped(padding:Sequence[sint]) -> tuple[tuple[sint, sint], ...]: r
 
 ReductionStr = Literal["mean", "sum", "none"]
 
-class Tensor(MathMixin, MovementMixin):
+class Tensor(MathTrait):
   """
   A `Tensor` is a multi-dimensional matrix containing elements of a single data type.
 

tinygrad/uop/mathtraits.py

@@ -1,5 +1,4 @@
-# mixins add syntactic sugar to Tensor and UOp
-from typing import TypeAlias, TYPE_CHECKING, Self
+from typing import TypeVar, TypeAlias, TYPE_CHECKING
 from tinygrad.uop import Ops
 from tinygrad.dtype import dtypes, ConstType
 from tinygrad.helpers import prod, argfix
@@ -7,14 +6,15 @@ if TYPE_CHECKING:
   from tinygrad.uop.ops import UOp
   sint:TypeAlias = UOp|int
 
-class MathMixin:
+TMT = TypeVar("TMT", bound="MathTrait")
+class MathTrait:
   # required to implement
-  def alu(self, op:Ops, *src:Self) -> Self: raise NotImplementedError
-  def const_like(self, b:ConstType) -> Self: raise NotImplementedError
+  def alu(self:TMT, op:Ops, *src:TMT) -> TMT: raise NotImplementedError
+  def const_like(self:TMT, b:ConstType) -> TMT: raise NotImplementedError
 
   # great functions you get!
-  def ufix(self, x:Self|ConstType) -> Self: return self.const_like(x) if not isinstance(x, MathMixin) else x
-  def _binop(self, op:Ops, x:Self|ConstType, reverse:bool) -> Self:
+  def ufix(self:TMT, x:TMT|ConstType) -> TMT: return self.const_like(x) if not isinstance(x, MathTrait) else x
+  def _binop(self:TMT, op:Ops, x:TMT|ConstType, reverse:bool) -> TMT:
     return self.ufix(x).alu(op, self) if reverse else self.alu(op, self.ufix(x))
   def logical_not(self): return self.ne(True)
   def neg(self):
@@ -24,7 +24,7 @@ class MathMixin:
     if (dtype:=getattr(self, 'dtype')) is not None:
       if isinstance(dtype, tuple): dtype = dtype[0]
       if not (dtypes.is_bool(dtype) or dtypes.is_int(dtype)): raise RuntimeError(f"{dtype} is not supported")
-  def add(self, x:Self|ConstType, reverse:bool=False):
+  def add(self:TMT, x:TMT|ConstType, reverse:bool=False):
     """
     Adds `self` and `x`.
     Equivalent to `self + x`.
@@ -42,7 +42,7 @@ class MathMixin:
     ```
     """
     return self._binop(Ops.ADD, x, reverse)
-  def mul(self, x:Self|ConstType, reverse:bool=False):
+  def mul(self:TMT, x:TMT|ConstType, reverse:bool=False):
     """
     Multiplies `self` and `x`.
     Equivalent to `self * x`.
@@ -61,7 +61,7 @@ class MathMixin:
     ```
     """
     return self._binop(Ops.MUL, x, reverse)
-  def bitwise_and(self, x:Self|ConstType, reverse:bool=False):
+  def bitwise_and(self:TMT, x:TMT|ConstType, reverse:bool=False):
     """
     Computes the bitwise AND of `self` and `x`.
     Equivalent to `self & x`.
@@ -75,7 +75,7 @@ class MathMixin:
     """
     self._check_dtype()
     return self._binop(Ops.AND, x, reverse)
-  def bitwise_or(self, x:Self|ConstType, reverse:bool=False):
+  def bitwise_or(self:TMT, x:TMT|ConstType, reverse:bool=False):
     """
     Computes the bitwise OR of `self` and `x`.
     Equivalent to `self | x`.
@@ -89,7 +89,7 @@ class MathMixin:
     """
     self._check_dtype()
     return self._binop(Ops.OR, x, reverse)
-  def bitwise_xor(self, x:Self|ConstType, reverse:bool=False):
+  def bitwise_xor(self:TMT, x:TMT|ConstType, reverse:bool=False):
     """
     Computes bitwise xor of `self` and `x`.
     Equivalent to `self ^ x`.
@@ -104,7 +104,7 @@ class MathMixin:
     """
     self._check_dtype()
     return self._binop(Ops.XOR, x, reverse)
-  def idiv(self, x:Self|ConstType, reverse:bool=False):
+  def idiv(self:TMT, x:TMT|ConstType, reverse:bool=False):
     """
     Divides `self` by `x`.
     Equivalent to `self // x`.
@@ -116,78 +116,78 @@ class MathMixin:
     ```
     """
     return self._binop(Ops.IDIV, x, reverse)
-  def mod(self, x:Self|ConstType, reverse:bool=False): return self._binop(Ops.MOD, x, reverse)
-  def sub(self, x:Self|ConstType, reverse:bool=False): return self.ufix(x).alu(Ops.ADD, -self) if reverse else self.alu(Ops.ADD, self.ufix(-x))
-  def div(self, x:Self|ConstType, reverse:bool=False):
+  def mod(self:TMT, x:TMT|ConstType, reverse:bool=False): return self._binop(Ops.MOD, x, reverse)
+  def sub(self:TMT, x:TMT|ConstType, reverse:bool=False): return self.ufix(x).alu(Ops.ADD, -self) if reverse else self.alu(Ops.ADD, self.ufix(-x))
+  def div(self:TMT, x:TMT|ConstType, reverse:bool=False):
     return (self.ufix(x)*self.alu(Ops.RECIPROCAL)) if reverse else (self*self.ufix(x).alu(Ops.RECIPROCAL))
 
   def __neg__(self): return self.neg()
 
-  def __add__(self, x:Self|ConstType): return self.add(x)
-  def __sub__(self, x:Self|ConstType): return self.sub(x)
-  def __mul__(self, x:Self|ConstType): return self.mul(x)
-  def __truediv__(self, x:Self|ConstType): return self.div(x)
-  def __floordiv__(self, x:Self|ConstType): return self.idiv(x)  # TODO: idiv is trunc div, not floordiv
-  def __mod__(self, x:Self|ConstType): return self.mod(x)
-  def __and__(self, x:Self|ConstType): return self.bitwise_and(x)
-  def __or__(self, x:Self|ConstType): return self.bitwise_or(x)
-  def __xor__(self, x:Self|ConstType): return self.bitwise_xor(x)
+  def __add__(self:TMT, x:TMT|ConstType): return self.add(x)
+  def __sub__(self:TMT, x:TMT|ConstType): return self.sub(x)
+  def __mul__(self:TMT, x:TMT|ConstType): return self.mul(x)
+  def __truediv__(self:TMT, x:TMT|ConstType): return self.div(x)
+  def __floordiv__(self:TMT, x:TMT|ConstType): return self.idiv(x)  # TODO: idiv is trunc div, not floordiv
+  def __mod__(self:TMT, x:TMT|ConstType): return self.mod(x)
+  def __and__(self:TMT, x:TMT|ConstType): return self.bitwise_and(x)
+  def __or__(self:TMT, x:TMT|ConstType): return self.bitwise_or(x)
+  def __xor__(self:TMT, x:TMT|ConstType): return self.bitwise_xor(x)
 
-  def __radd__(self, x:Self|ConstType): return self.add(x, True)
-  def __rsub__(self, x:Self|ConstType): return self.sub(x, True)
-  def __rmul__(self, x:Self|ConstType): return self.mul(x, True)
-  def __rtruediv__(self, x:Self|ConstType): return self.div(x, True)
-  def __rfloordiv__(self, x:Self|ConstType): return self.idiv(x, True)
-  def __rand__(self, x:Self|ConstType): return self.bitwise_and(x, True)
-  def __ror__(self, x:Self|ConstType): return self.bitwise_or(x, True)
-  def __rxor__(self, x:Self|ConstType): return self.bitwise_xor(x, True)
-  def __rmod__(self, x:Self|ConstType): return self.mod(x, True)
+  def __radd__(self:TMT, x:TMT|ConstType): return self.add(x, True)
+  def __rsub__(self:TMT, x:TMT|ConstType): return self.sub(x, True)
+  def __rmul__(self:TMT, x:TMT|ConstType): return self.mul(x, True)
+  def __rtruediv__(self:TMT, x:TMT|ConstType): return self.div(x, True)
+  def __rfloordiv__(self:TMT, x:TMT|ConstType): return self.idiv(x, True)
+  def __rand__(self:TMT, x:TMT|ConstType): return self.bitwise_and(x, True)
+  def __ror__(self:TMT, x:TMT|ConstType): return self.bitwise_or(x, True)
+  def __rxor__(self:TMT, x:TMT|ConstType): return self.bitwise_xor(x, True)
+  def __rmod__(self:TMT, x:TMT|ConstType): return self.mod(x, True)
 
-  def __lt__(self, x:Self|ConstType): return self.alu(Ops.CMPLT, self.ufix(x))
-  def __gt__(self, x:Self|ConstType): return self.ufix(x).alu(Ops.CMPLT, self)
-  def __ge__(self, x:Self|ConstType): return (self < x).logical_not()
-  def __le__(self, x:Self|ConstType): return (self > x).logical_not()
+  def __lt__(self:TMT, x:TMT|ConstType): return self.alu(Ops.CMPLT, self.ufix(x))
+  def __gt__(self:TMT, x:TMT|ConstType): return self.ufix(x).alu(Ops.CMPLT, self)
+  def __ge__(self:TMT, x:TMT|ConstType): return (self < x).logical_not()
+  def __le__(self:TMT, x:TMT|ConstType): return (self > x).logical_not()
 
-  def ne(self, x:Self|ConstType): return self.alu(Ops.CMPNE, self.ufix(x))
-  def eq(self, x:Self|ConstType): return self.ne(x).logical_not()
-  def __ne__(self, x:Self|ConstType): return self.ne(x)  # type: ignore[override]
+  def ne(self:TMT, x:TMT|ConstType): return self.alu(Ops.CMPNE, self.ufix(x))
+  def eq(self:TMT, x:TMT|ConstType): return self.ne(x).logical_not()
+  def __ne__(self:TMT, x:TMT|ConstType): return self.ne(x)  # type: ignore[override]
   # NOTE: __eq__ isn't overridden, and means the same thing as is by default
 
-  def lshift(self, x:Self|int, reverse:bool=False): return self._binop(Ops.SHL, x, reverse)
-  def rshift(self, x:Self|int, reverse:bool=False): return self._binop(Ops.SHR, x, reverse)
-  def __lshift__(self, x:Self|int): return self.lshift(x)
-  def __rshift__(self, x:Self|int): return self.rshift(x)
-  def __rlshift__(self, x:Self|int): return self.lshift(x, True)
-  def __rrshift__(self, x:Self|int): return self.rshift(x, True)
+  def lshift(self:TMT, x:TMT|int, reverse:bool=False): return self._binop(Ops.SHL, x, reverse)
+  def rshift(self:TMT, x:TMT|int, reverse:bool=False): return self._binop(Ops.SHR, x, reverse)
+  def __lshift__(self:TMT, x:TMT|int): return self.lshift(x)
+  def __rshift__(self:TMT, x:TMT|int): return self.rshift(x)
+  def __rlshift__(self:TMT, x:TMT|int): return self.lshift(x, True)
+  def __rrshift__(self:TMT, x:TMT|int): return self.rshift(x, True)
 
-  def maximum(self, x:Self|ConstType): return self.alu(Ops.MAX, self.ufix(x))
-  def minimum(self, x:Self|ConstType): return -(-self).maximum(-x)
-  def where(self, x:Self|ConstType, y:Self|ConstType):
+  def maximum(self:TMT, x:TMT|ConstType): return self.alu(Ops.MAX, self.ufix(x))
+  def minimum(self:TMT, x:TMT|ConstType): return -(-self).maximum(-x)
+  def where(self:TMT, x:TMT|ConstType, y:TMT|ConstType):
     if isinstance(x, type(self)): return self.alu(Ops.WHERE, x, x.ufix(y))
     if isinstance(y, type(self)): return self.alu(Ops.WHERE, y.ufix(x), y)
     raise RuntimeError("where needs at least one UOp arg")
-  def threefry(self, seed:Self): return self.alu(Ops.THREEFRY, seed)
+  def threefry(self:TMT, seed:TMT): return self.alu(Ops.THREEFRY, seed)
   def reciprocal(self): return self.alu(Ops.RECIPROCAL)
   def trunc(self): return self.alu(Ops.TRUNC)
   def sqrt(self): return self.alu(Ops.SQRT)
   def sin(self): return self.alu(Ops.SIN)
   def log2(self): return self.alu(Ops.LOG2)
   def exp2(self): return self.alu(Ops.EXP2)
-  def pow(self, x:Self|ConstType): return self.alu(Ops.POW, self.ufix(x))
-  def __pow__(self, x:Self|ConstType): return self.pow(x)
+  def pow(self:TMT, x:TMT|ConstType): return self.alu(Ops.POW, self.ufix(x))
+  def __pow__(self:TMT, x:TMT|ConstType): return self.pow(x)
+
+  # **** movement ops ****
 
-class MovementMixin:
   # required to implement
-  def _mop(self, op:Ops, arg) -> Self: raise NotImplementedError
+  def _mop(self:TMT, op:Ops, arg) -> TMT: raise NotImplementedError
   @property
   def shape(self) -> tuple["sint", ...]: raise NotImplementedError
 
-  # great functions you get!
-  def view(self, shape, *args) -> Self:
+  def view(self:TMT, shape, *args) -> TMT:
     """`.view` is an alias for `.reshape`."""
     return self.reshape(shape, *args)
 
-  def reshape(self, shape, *args) -> Self:
+  def reshape(self:TMT, shape, *args) -> TMT:
     """
     Returns a tensor with the same data as the original tensor but with a different shape.
     `shape` can be passed as a tuple or as separate arguments.

tinygrad/uop/ops.py

@@ -4,7 +4,7 @@ import sys, time, functools, itertools, math, operator, hashlib, os, types, pick
 from dataclasses import dataclass
 from enum import Enum, auto
 from tinygrad.uop import Ops, GroupOp
-from tinygrad.uop.mixins import MathMixin, MovementMixin
+from tinygrad.uop.mathtraits import MathTrait
 from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate, PtrDType, least_upper_dtype, Invalid, InvalidType, AddrSpace
 from tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, flatten, TRACEMETA
 from tinygrad.helpers import PICKLE_BUFFERS, PROFILE, dedup, cdiv, cmod, diskcache_put, to_function_name, cpu_profile, TracingKey, VIZ, SPEC, CI
@@ -104,7 +104,7 @@ class recursive_property(property):
 
 # NOTE: this should be frozen, but frozen is slower
 @dataclass(eq=False, slots=True)
-class UOp(MathMixin, MovementMixin, metaclass=UOpMetaClass):
+class UOp(MathTrait, metaclass=UOpMetaClass):
   op:Ops
   dtype:DType = dtypes.void
   src:tuple[UOp, ...] = tuple()
@@ -853,7 +853,7 @@ def printable(loc:tuple[str, int]) -> str:
   try: return lines(loc[0])[loc[1]-1].strip()
   except FileNotFoundError: return "<missing>"
 
-class UPat(MathMixin, MovementMixin):
+class UPat(MathTrait):
   __slots__ = ("op", "dtype", "arg", "name", "src")
   def __init__(self, op:Ops|tuple[Ops, ...]|set[Ops]|None=None, dtype:DType|tuple[DType, ...]|None=None,
                src:tuple[UPat, ...]|list[UPat]|UPat|None=None, arg:Any=None,