move _pool to MovementMixins (#13257)

tinygrad/mixin/movement.py

@@ -2,7 +2,8 @@
 import functools
 from typing import TypeAlias, TYPE_CHECKING, Self
 from tinygrad.uop import Ops
-from tinygrad.helpers import prod, argfix, flatten, dedup
+from tinygrad.helpers import prod, argfix, flatten, dedup, make_tuple, ceildiv
+from tinygrad.uop.ops import resolve, smax
 if TYPE_CHECKING: from tinygrad.uop.ops import UOp
 sint: TypeAlias = "UOp | int"
 
@@ -326,3 +327,24 @@ class MovementMixin:
     expanded_shape = flatten([[s] if r == 1 else [r, s] for r,s in zip(repeats, base_shape)])
     final_shape = [r*s for r,s in zip(repeats, base_shape)]
     return self.reshape(unsqueezed_shape).expand(expanded_shape).reshape(final_shape)
+
+  # **** pool level ****
+
+  def _pool(self, k_:tuple[sint, ...], stride:int|tuple[int, ...]=1, dilation:int|tuple[int, ...]=1) -> Self:
+    assert len(self.shape) >= len(k_), f"can't pool {self.shape} with {k_}"
+    s_, d_ = make_tuple(stride, len(k_)), make_tuple(dilation, len(k_))
+    assert len(k_) == len(s_) == len(d_), f"stride/dilation mismatch kernel:{k_} stride:{s_} dilation:{d_}"
+    noop, i_ = [None] * (self.ndim-len(k_)), self.shape[-len(k_):]
+    assert all(resolve(d*(k-1)+1 <= i) for k,d,i in zip(k_,d_,i_)), "kernel size cannot be greater than actual input size"
+    o_ = [ceildiv(i-d*(k-1), s) for i,d,k,s in zip(i_,d_,k_,s_)]
+    # input size scaling factor to make sure shrink for stride is possible
+    f_ = [smax(1, ceildiv(o*s - d, i)) for o,s,i,d in zip(o_,s_,i_,d_)]
+    # repeats such that we don't need padding
+    x = self.repeat([1]*len(noop) + [ceildiv(k*(i*f+d),i) for k,i,d,f in zip(k_,i_,d_,f_)])
+    # handle dilation
+    x = x.shrink_to(noop + [k*(i*f+d) for k,i,d,f in zip(k_,i_,d_,f_)]).reshape(noop + flatten((k,(i*f+d)) for k,i,d,f in zip(k_,i_,d_,f_)))
+    # handle stride
+    x = x.shrink_to(noop + flatten((k,o*s) for k,o,s in zip(k_,o_,s_))).reshape(noop + flatten((k,o,s) for k,o,s in zip(k_,o_,s_)))
+    x = x.shrink_to(noop + flatten((k,o,1) for k,o in zip(k_,o_))).reshape(noop + flatten((k,o) for k,o in zip(k_,o_)))
+    # permute to move reduce to the end
+    return x.permute(*range(len(noop)), *[len(noop)+i*2+1 for i in range(len(i_))], *[len(noop)+i*2 for i in range(len(i_))])

tinygrad/tensor.py

@@ -2093,25 +2093,6 @@ class Tensor(OpMixin):
 
   # ***** processing ops *****
 
-  def _pool(self, k_:tuple[sint, ...], stride:int|tuple[int, ...]=1, dilation:int|tuple[int, ...]=1) -> Tensor:
-    assert len(self.shape) >= len(k_), f"can't pool {self.shape} with {k_}"
-    s_, d_ = make_tuple(stride, len(k_)), make_tuple(dilation, len(k_))
-    assert len(k_) == len(s_) == len(d_), f"stride/dilation mismatch kernel:{k_} stride:{s_} dilation:{d_}"
-    noop, i_ = [None] * (self.ndim-len(k_)), self.shape[-len(k_):]
-    assert all(resolve(d*(k-1)+1 <= i) for k,d,i in zip(k_,d_,i_)), "kernel size cannot be greater than actual input size"
-    o_ = [ceildiv(i-d*(k-1), s) for i,d,k,s in zip(i_,d_,k_,s_)]
-    # input size scaling factor to make sure shrink for stride is possible
-    f_ = [smax(1, ceildiv(o*s - d, i)) for o,s,i,d in zip(o_,s_,i_,d_)]
-    # repeats such that we don't need padding
-    x = self.repeat([1]*len(noop) + [ceildiv(k*(i*f+d),i) for k,i,d,f in zip(k_,i_,d_,f_)])
-    # handle dilation
-    x = x.shrink_to(noop + [k*(i*f+d) for k,i,d,f in zip(k_,i_,d_,f_)]).reshape(noop + flatten((k,(i*f+d)) for k,i,d,f in zip(k_,i_,d_,f_)))
-    # handle stride
-    x = x.shrink_to(noop + flatten((k,o*s) for k,o,s in zip(k_,o_,s_))).reshape(noop + flatten((k,o,s) for k,o,s in zip(k_,o_,s_)))
-    x = x.shrink_to(noop + flatten((k,o,1) for k,o in zip(k_,o_))).reshape(noop + flatten((k,o) for k,o in zip(k_,o_)))
-    # permute to move reduce to the end
-    return x.permute(*range(len(noop)), *[len(noop)+i*2+1 for i in range(len(i_))], *[len(noop)+i*2 for i in range(len(i_))])
-
   def _resolve_pool_pads(self, padding:int|Sequence[int], dims:int) -> Sequence[int]:
     if not isinstance(padding, int) and not (len(padding) == 2*dims or len(padding) == dims):
       raise ValueError(f"Padding must be an int or a sequence of length {dims} or {2*dims}, but got {padding=} for {self.shape=} with {dims=}.")

tinygrad/uop/ops.py

@@ -4,7 +4,6 @@ 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.mixin import OpMixin
 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
@@ -107,6 +106,9 @@ class recursive_property(property):
         s.__dict__[self.nm] = val = self.fxn(s)
     return val
 
+# we import this late so we can use resolve/smax in mixins
+from tinygrad.mixin import OpMixin
+
 # NOTE: this should be frozen, but frozen is slower
 @dataclass(eq=False, slots=True)
 class UOp(OpMixin, metaclass=UOpMetaClass):