XLA itself does not consume these, but they can be propagated onto scatter() when computing gradients.
Compute unique/sorted information on indexed accesses and indexed updates. Non-advanced indexes are always sorted and unique.
Previously we allowed a dimension variable in lieu of a dimension. Now we
allow multi-variate dimension polynomials. These polynomials overload addition, subtraction,
multiplication. They also partially support equality and inequality checking.
Equality and inequality are supported only when the operation result is the
same for all valuations of variables greater than 0. For example, `a == a`,
`a * b + 1 == 1 + b * a`, `a >= 1`, `2 * a + b >= 3`, `a >= a`. However, for
the following a `core.InconclusiveDimensionOperation` is raised: `a = b`, `a
>= 2`.
Division is supported only in the cases when either there is no remainder,
or the divisor is a constant.
This change allows us to support more general cases of `jnp.reshape(-1)`,
such as those used in the internal implementation of `random_gamma`:
```
y = x.reshape((2, -1))
z = ... y ...
return z.reshape(x.shape)
```
Part of the discrepancies were due to JAX using a workaround for
missing complex convolutions on CPU/GPU, while jax2tf was not using
it. We apply the same lowering as JAX, on all platforms.
This allows us to remove custom numeric tolerances and enables complex
convolutions on GPU.
PiperOrigin-RevId: 374199441
Previously we simply converted integer_pow to tf.math.pow. JAX instead uses
a series of multiplications. We now use the same lowering strategy as JAX, so
that we have the same numerical result.
Also improved the error messages for assertion failures.
PiperOrigin-RevId: 373351147
--
1ecf4f02891cad70cc8f094b49cf2458105ca366 by George Necula <gcnecula@gmail.com>:
[jax2tf] Change the conversion of dot_general to use XLA op.
Instead of converting the dot_general to a sea of TF ops, when
we enable_xla we just use the XLA op. This has the advantage
that it also supports the preferred_element_type.
Fixed bug with passing the precision parameter to TF.
Also improved tests to print the HLO in case of numerical errors.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/jax/pull/6717 from gnecula:tf_dot 1ecf4f02891cad70cc8f094b49cf2458105ca366
PiperOrigin-RevId: 373326655
Previously, in order to increase the coverage of masking we added special
cases in lax.py and lax_numpy.py to avoid exceptions in presence of
masking.Poly.
For example:
```
if not isinstance(d, masking.Poly):
if some_check(d):
raise ValueError
```
All such conditionals make the code behave potentially different when
tracing with masking.Poly than when tracing with concrete shapes, which
makes it hard to ensure soundness.
Perhaps the most eggregious was:
```
if type(i) is Poly:
# dummy index if i is polynomial, doesn't matter for shape inference
i = 0
```
