Move PallasQTensor materialization code as a method. Call materiali…

…zation before `dequant`. This simplifies user interface.

PiperOrigin-RevId: 636916800

aqt/jax/v2/transpose.py

@@ -26,14 +26,78 @@
 import jax.numpy as jnp
 
 
+def transpose(t: jax.Array, axes: list[utils.AxisIdx]):
+  """Transpose tensor through reshape if possible.
+
+  This is a workaround to use AQT inside pallas kernel. Pallas support
+  transposing only 2D tensor. However, tensor with a specific pattern can be
+  transposed through reshaping. This pattern is observed a lot in scale factor
+  of QTensor.
+
+  If axes with dimensions larger than one are ordered in ascending order, then
+  the tensor can be transposed by reshaping. For example,
+
+  Let's say the shape of tensor `x` is [10, 1, 1]. Then, there are only one
+  axis with dimension larger than one. The tensor can be transposed by
+  reshaping no matter what the order of permutation, e.g., `jnp.transpose(x, [1,
+  2, 0]) == jnp.reshape(x, [x.shape[i] for i in [1, 2, 0]])`.
+
+  Another example is `x` with shape [10, 1, 30]. Then, there are two axes with
+  dimension larger than one. The tensor can be transposed by reshaping if
+  tensor is permuted while maintining the order of axes with dimension larger
+  than one, i.e., the first axes remains in front of third axis after
+  permutation, e.g.,  `jnp.transpose(x, [0, 2, 1]) == jnp.reshape(x, [x.shape[i]
+  for i in [0, 2, 1]])`.
+
+  Args:
+    t: input tensor.
+    axes: transpose axes.
+
+  Returns:
+    A transposed tensor.
+
+  Example:
+    >>> x = np.random.randn([10, 1, 1]) # x is transposable by reshaping.
+    >>> axes = [2, 1, 0]
+    >>> y = jnp.transpose(x, axes)
+    >>> (jnp.reshape(x, y.shape) == y).all()
+    True
+    >>> (transpose(x, axes) == y).all()
+
+    >>> x = np.random.randn([10, 20, 30]) # x is not transposable by reshaping.
+    >>> axes = [2, 1, 0]
+    >>> y = jnp.transpose(x, axes)
+    >>> (jnp.reshape(x, y.shape) == y).all()
+    False
+    >>> (transpose(x, axes) == y).all()
+    True
+  """
+
+  axes_bigger_than_one = [i for i, size in enumerate(t.shape) if size > 1]
+  permutation_axes_bigger_than_one = [
+      axes.index(i) for i in axes_bigger_than_one
+  ]
+  # Check if axes bigger than one are ordered in ascending order.
+  reshapable = (
+      sorted(permutation_axes_bigger_than_one)
+      == permutation_axes_bigger_than_one
+  )
+
+  if reshapable:
+    t = jnp.reshape(t, (t.shape[i] for i in axes))
+  else:
+    t = jax.lax.transpose(t, axes)
+  return t
+
+
 def _scale_trans(x, ca, ba):
   """Transposes x to output dimension order."""
   ca = list(ca)
   ba = list(ba)
   for i in ca:
     assert x.shape[i] == 1
   ra = utils.get_remaining_axes(x.ndim, ca, ba)
-  x = jnp.transpose(x, ba + ra + ca)
+  x = transpose(x, ba + ra + ca)
   # TODO(lew): x = jnp.squeeze(x, axis=range(len(ba+ra): len(x.shape))
   shape_ba = x.shape[: len(ba)]
   shape_ra = x.shape[len(ba) : len(x.shape) - len(ca)]
@@ -107,7 +171,7 @@ def _scale_trans_back(
 
   assert -1 not in transpose_back
 
-  scale = jnp.transpose(scale, transpose_back)
+  scale = transpose(scale, transpose_back)
   return scale
 
 
@@ -183,7 +247,7 @@ def _scale_trans_for_other_input(
   assert ra_idx == len(my_ra)
 
   # Transpose.
-  x = jnp.transpose(x, transpose_dim)
+  x = transpose(x, transpose_dim)
 
   # Remove redundant axis.
   if len(x.shape) > other_rank:

aqt/jax/v2/transpose_test.py

@@ -14,6 +14,7 @@
 
 """Tests for transpose."""
 
+import math
 from absl.testing import absltest
 from absl.testing import parameterized
 from aqt.jax.v2 import transpose
@@ -22,6 +23,20 @@
 
 class AqtTransposeTest(parameterized.TestCase):
 
+  @parameterized.parameters(
+      ((10, 1, 10), (0, 2, 1)),
+      ((10, 1, 5), (1, 0, 2)),
+      ((5, 1, 10), (0, 1, 2)),
+      ((10, 20, 30), (1, 2, 0)),
+      ((10, 1, 1), (0, 2, 1)),
+      ((10, 1, 1), (2, 1, 0)),
+      ((10, 1, 1), (1, 2, 0)),
+  )
+  def test_transpose(self, tensor_shape, transpose_axes):
+    t = jnp.arange(math.prod(tensor_shape)).reshape(tensor_shape)
+    t_t = transpose.transpose(t, transpose_axes)
+    self.assertTrue((t_t == jnp.transpose(t, transpose_axes)).all())
+
   @parameterized.parameters(
       # 'bmnts,bsnh->bmtnh'
       (