confirm it works in amd_uop_matmul

extra/gemm/amd_uop_matmul.py

@@ -88,15 +88,15 @@ def hand_spec_kernel3():
   # ---------------------------
   # GLOBAL -> LOCAL (As, Bs)
   # ---------------------------
-  b = b.reshape((N // BLOCK_K, BLOCK_K,
+  b = b.reshape(N // BLOCK_K, BLOCK_K,
-                 N // BLOCK_N, BLOCK_N))
+                N // BLOCK_N, BLOCK_N)
   i = UOp.range(BLOCK_N * BLOCK_K // THREADS_PER_BLOCK, 1)
   index_x = tid % BLOCK_N
   index_y = (tid // BLOCK_N) + (THREADS_PER_BLOCK // BLOCK_N) * i
   Bs_store = Bs[index_y, index_x].store(b[k_tile_range, index_y, blockIdx_x, index_x]).end(i)
 
-  a = a.reshape((N // BLOCK_M, BLOCK_M,
+  a = a.reshape(N // BLOCK_M, BLOCK_M,
-                 N // BLOCK_K, BLOCK_K))
+                N // BLOCK_K, BLOCK_K)
   i = UOp.range(BLOCK_M * BLOCK_K // THREADS_PER_BLOCK, 2)
   index_x = tid % BLOCK_K
   index_y = (tid // BLOCK_K) + (THREADS_PER_BLOCK // BLOCK_K) * i
@@ -113,12 +113,12 @@ def hand_spec_kernel3():
   # ---------------------------
   # LOCAL -> REG (per-wave tiles)
   # ---------------------------
-  Bs_view = Bs.reshape((BLOCK_K, WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN))
+  Bs_view = Bs.reshape(BLOCK_K, WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN)
   iterWaveN = UOp.range(ITERS_PER_WAVE_N, 4)
   i = UOp.range(TN, 5)
   B_row = B_row[iterWaveN, i].set(Bs_view[k, waveIdx, iterWaveN, idxInWave, i], end=(iterWaveN, i))
 
-  As_view = As.reshape((BLOCK_K, WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM))
+  As_view = As.reshape(BLOCK_K, WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM)
   iterWaveM = UOp.range(ITERS_PER_WAVE_M, 6)
   i = UOp.range(TM, 7)
   A_col = A_col[iterWaveM, i].set(As_view[k, waveIdy, iterWaveM, idyInWave, i], end=(iterWaveM, i))
@@ -139,8 +139,8 @@ def hand_spec_kernel3():
   # ---------------------------
   # REG -> GLOBAL (epilogue)
   # ---------------------------
-  c = c.reshape((N//BLOCK_M, WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM,
+  c = c.reshape(N//BLOCK_M, WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM,
-                 N//BLOCK_N, WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN))
+                N//BLOCK_N, WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN)
   iterWaveM = UOp.range(ITERS_PER_WAVE_M, 1000)
   yt = UOp.range(TM, 1001)
   iterWaveN = UOp.range(ITERS_PER_WAVE_N, 1002)

tinygrad/tensor.py

@@ -10,7 +10,7 @@ from tinygrad.helpers import IMAGE, WINO, Metadata, TRACEMETA, ceildiv, fetch, p
 from tinygrad.helpers import suppress_finalizing
 from tinygrad.gradient import compute_gradient
 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, srender
+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
 from tinygrad.engine.realize import run_schedule
@@ -1038,28 +1038,7 @@ class Tensor(MathTrait):
 
   # ***** movement low level ops *****
 
-  def view(self, shape:tuple[sint, ...], *args) -> Tensor:
+  def _mop(self, op:Ops, arg) -> Tensor: return self._apply_uop(UOp._mop, extra_args=(op,), arg=arg)
-    """`.view` is an alias for `.reshape`."""
-    return self.reshape(shape, *args)
-
-  def reshape(self, shape, *args) -> Tensor:
-    """
-    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.
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    t = Tensor.arange(6)
-    print(t.reshape(2, 3).numpy())
-    ```
-    """
-    # resolve None and args
-    new_shape = tuple([s if s is not None else self.shape[i] for i,s in enumerate(argfix(shape, *args))])
-    # resolve -1
-    if (c := new_shape.count(-1)) > 1: raise RuntimeError(f"only one dimension can be inferred using -1, getting {new_shape}")
-    if c: new_shape = tuple([-prod(self.shape) // prod(new_shape) if s == -1 else s for s in new_shape])
-    if resolve(prod(self.shape) != prod(new_shape), True):
-      raise ValueError(f"size mismatch, can't reshape ({', '.join(srender(d) for d in self.shape)}) -> ({', '.join(srender(d) for d in new_shape)})")
-    return self._apply_uop(UOp.reshape, arg=new_shape) if new_shape != self.shape else self
 
   def expand(self, shape, *args) -> Tensor:
     """

tinygrad/uop/mathtraits.py

@@ -1,6 +1,10 @@
-from typing import TypeVar
+from typing import TypeVar, TypeAlias, TYPE_CHECKING
 from tinygrad.uop import Ops
 from tinygrad.dtype import dtypes, ConstType
+from tinygrad.helpers import prod, argfix
+if TYPE_CHECKING:
+  from tinygrad.uop.ops import UOp
+  sint:TypeAlias = UOp|int
 
 TMT = TypeVar("TMT", bound="MathTrait")
 class MathTrait:
@@ -171,3 +175,32 @@ class MathTrait:
   def exp2(self): return self.alu(Ops.EXP2)
   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 ****
+
+  # required to implement
+  def _mop(self:TMT, op:Ops, arg) -> TMT: raise NotImplementedError
+  @property
+  def shape(self) -> tuple["sint", ...]: raise NotImplementedError
+
+  def view(self:TMT, shape, *args) -> TMT:
+    """`.view` is an alias for `.reshape`."""
+    return self.reshape(shape, *args)
+
+  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.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor.arange(6)
+    print(t.reshape(2, 3).numpy())
+    ```
+    """
+    # resolve None and args
+    new_shape = tuple([s if s is not None else self.shape[i] for i,s in enumerate(argfix(shape, *args))])
+    # resolve -1
+    if (c := new_shape.count(-1)) > 1: raise RuntimeError(f"only one dimension can be inferred using -1, getting {new_shape}")
+    if c: new_shape = tuple([-prod(self.shape) // prod(new_shape) if s == -1 else s for s in new_shape])
+    if prod(self.shape) != prod(new_shape): raise ValueError(f"size mismatch, can't reshape ({self.shape}) -> ({new_shape})")
+    return self._mop(Ops.RESHAPE, arg=new_shape) if new_shape != self.shape else self

tinygrad/uop/ops.py

@@ -533,7 +533,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
 
   # in these four, if the shape doesn't change we can return self
   def forced_reshape(self, arg:tuple[sint, ...]): return self._mop(Ops.RESHAPE, arg, same_shape_noop=False)
-  def reshape(self, arg:tuple[sint, ...]): return self._mop(Ops.RESHAPE, arg, same_shape_noop=True)
+  #def reshape(self, arg:tuple[sint, ...]): return self._mop(Ops.RESHAPE, arg, same_shape_noop=True)
   def expand(self, arg:tuple[sint, ...]): return self._mop(Ops.EXPAND, arg, same_shape_noop=True)
   def shrink(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.SHRINK, arg, same_shape_noop=True)
   def pad(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.PAD, arg, same_shape_noop=True)