Caching Free Integrated Aesara Objective #629
Conversation
Codecov Report
@@ Coverage Diff @@
## develop #629 +/- ##
============================================
- Coverage 88.16% 34.33% -53.84%
============================================
Files 79 93 +14
Lines 5257 5884 +627
============================================
- Hits 4635 2020 -2615
- Misses 622 3864 +3242
Continue to review full report at Codecov.
|
|
|
||
| return ret | ||
|
|
||
| def __deepcopy__(self, memodict=None): |
There was a problem hiding this comment.
is serialization of aesara objects possible?
There was a problem hiding this comment.
I think pickling would require some additional work, see https://aesara.readthedocs.io/en/latest/library/compile/function.html#aesara.compile.function.function_dump
There was a problem hiding this comment.
I haven't tested it though.
| log_prob = self._coeff * self._objective.inner_ret[FVAL] | ||
| outputs[0][0] = np.array(log_prob) | ||
|
|
||
| def grad(self, inputs, g): |
There was a problem hiding this comment.
howby what dark magic is it possible that inputs is not used here? 🤔
There was a problem hiding this comment.
Added a note about this. The trick is that we already know what the inputs here are going to look like when computing inner_ret, so we can ignore them here and just use the precomputed values. This also has the advantage that we only spend a minimal amount of time in aesara functions, since the bulk of computation is done in the outer call_unprocessed, which improves performance in multithreaded settings as we avoid a lot of locking of aesara functions.
Co-authored-by: Yannik Schälte <31767307+yannikschaelte@users.noreply.github.com>
Simplifies the construction of aesara objectives. This implementation allows exploiting integrated evaluation of the wrapped objective.