From f2bd65ccfc72c40151f5a04831c155444c9a0e74 Mon Sep 17 00:00:00 2001
From: qazal <77887910+Qazalin@users.noreply.github.com>
Date: Thu, 3 Apr 2025 12:29:02 +0800
Subject: [PATCH] delete Ops.EMPTY and Tensor._metaop (#9715)

* delete Ops.EMPTY and Tensor._metaop [pr]

* test_creation

* arg=

* abstractions2
---
 docs/abstractions2.py |  4 ++--
 tinygrad/ops.py       | 13 +++----------
 tinygrad/tensor.py    | 19 ++++++++-----------
 3 files changed, 13 insertions(+), 23 deletions(-)

diff --git a/docs/abstractions2.py b/docs/abstractions2.py
index 1998cb3383..60cef6824d 100644
--- a/docs/abstractions2.py
+++ b/docs/abstractions2.py
@@ -80,8 +80,8 @@ from tinygrad.engine.realize import run_schedule
 from tinygrad.engine.schedule import create_schedule_with_vars
 
 # allocate some values + load in values
-a = UOp.metaop(Ops.EMPTY, (1,), dtypes.int32, DEVICE)
-b = UOp.metaop(Ops.EMPTY, (1,), dtypes.int32, DEVICE)
+a = UOp.new_buffer(DEVICE, 1, dtypes.int32)
+b = UOp.new_buffer(DEVICE, 1, dtypes.int32)
 a.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
 b.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
 
diff --git a/tinygrad/ops.py b/tinygrad/ops.py
index 546ad70cd2..11f061a42e 100644
--- a/tinygrad/ops.py
+++ b/tinygrad/ops.py
@@ -95,9 +95,6 @@ class Ops(FastEnum):
   # uops that aren't rendered
   NAME = auto(); SINK = auto(); CONTIGUOUS = auto(); CONTIGUOUS_BACKWARD = auto(); DETACH = auto(); KERNEL = auto(); UNIQUE = auto() # noqa: E702
 
-  # TODO: empty continues to exist because of tensor
-  EMPTY = auto()
-
   # MetaOps
   COPY = auto(); BUFFER_VIEW = auto() # noqa: E702
 
@@ -474,13 +471,9 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       return UOp.const(dtype, unwrap(arg)).replace(src=(UOp(Ops.VIEW, dtypes.void, (UOp(Ops.DEVICE, arg=device),),
                  ShapeTracker.from_shape(())),)).reshape((1,)*len(shape)).expand(shape)
     # Tensor variable binding is BIND(VAR(VIEW(DEVICE)), CONST(VIEW(DEVICE)))
-    if op is Ops.BIND:
-      var, val = arg.unbind()
-      return var.replace(src=(UOp(Ops.VIEW, dtypes.void, (UOp(Ops.DEVICE, arg=device),), ShapeTracker.from_shape(shape)),)).bind(val)
-    # otherwise it's just a RESHAPE(BUFFER)
-    assert op is Ops.EMPTY, f"unkown op {op}"
-    if not isinstance(size:=prod([x.vmax if isinstance(x, UOp) else x for x in shape]), int): raise ValueError(f"size must be int {size}")
-    return UOp.new_buffer(device, size, dtype).reshape(shape)
+    assert op is Ops.BIND, f"unkown op {op}"
+    var, val = arg.unbind()
+    return var.replace(src=(UOp(Ops.VIEW, dtypes.void, (UOp(Ops.DEVICE, arg=device),), ShapeTracker.from_shape(shape)),)).bind(val)
   def copy_to_device(self, device:str|tuple[str, ...], clone:bool=False): return UOp(Ops.COPY, self.dtype, (UOp(Ops.DEVICE, arg=device), self), clone)
   def clone(self) -> UOp: return self.copy_to_device(self.device, clone=True)
   @property
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 1c6eaf6944..ac0daf2c19 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -142,7 +142,7 @@ class Tensor(SimpleMathTrait):
     if isinstance(data, UOp):
       assert dtype is None or dtype==data.dtype, "dtype doesn't match, and casting isn't supported"
       if data.op is Ops.BIND: data = _metaop(Ops.BIND, tuple(), dtype or data.dtype, device, data)
-    elif data is None: data = _metaop(Ops.EMPTY, (0,), dtype or dtypes.default_float, device)
+    elif data is None: data = _metaop(Ops.CONST, (0,), dtype or dtypes.default_float, device, arg=0)
     elif isinstance(data, get_args(ConstType)): data = _metaop(Ops.CONST, tuple(), dtype or dtypes.from_py(data), device, data)
     elif isinstance(data, bytes): data = _frompy(data, dtypes.uint8 if dtype is None else dtype)
     elif isinstance(data, (list, tuple)):
@@ -406,15 +406,7 @@ class Tensor(SimpleMathTrait):
   # ***** creation entrypoint *****
 
   @staticmethod
-  def _metaop(op, shape, device:str|tuple[str, ...]|None=None, dtype:DTypeLike|None=None, arg=None, **kwargs) -> Tensor:
-    dtype = to_dtype(dtype) if dtype is not None else dtypes.default_float
-    if isinstance(device, tuple):
-      return Tensor(UOp.multi(*[UOp.metaop(op, shape, dtype, Device.canonicalize(d), arg) for d in device], axis=None),
-                    device, dtype, **kwargs)
-    return Tensor(UOp.metaop(op, shape, dtype, Device.canonicalize(device), arg), device, dtype, **kwargs)
-
-  @staticmethod
-  def empty(*shape, **kwargs) -> Tensor:
+  def empty(*shape, device:str|tuple[str, ...]|None=None, dtype:DTypeLike|None=None, **kwargs) -> Tensor:
     """
     Creates an empty tensor with the given shape.
 
@@ -426,7 +418,12 @@ class Tensor(SimpleMathTrait):
     print(t.shape)
     ```
     """
-    return Tensor._metaop(Ops.EMPTY, argfix(*shape), **kwargs)
+    dtype, shape = to_dtype(dtype) if dtype is not None else dtypes.default_float, argfix(*shape)
+    if not isinstance(size:=prod([x.vmax if isinstance(x, UOp) else x for x in shape]), int): raise ValueError(f"size must be int {size}")
+    if isinstance(device, tuple):
+      return Tensor(UOp.multi(*[UOp.new_buffer(Device.canonicalize(d), size, dtype).reshape(shape) for d in device], axis=None),
+                    device, dtype, **kwargs)
+    return Tensor(UOp.new_buffer(Device.canonicalize(device), size, dtype), device, dtype, **kwargs).reshape(shape)
 
   @staticmethod
   def from_blob(ptr:int, shape:tuple[int, ...], **kwargs) -> Tensor: