This change, when enabled, stages out all primitive calls in the dynamic
scope of a jitted, pmapped, or control flow function, rather than only
staging out based on data dependence. One improvement is that jitted
functions can consume less memory, by avoiding instantiating large
constants at trace time, and cause less memory fragmentation as well. It
also simplifies several internals.
See https://github.com/google/jax/pull/3370 fo more information.
* moved check_jaxpr code around to match eval_jaxpr
This change is mostly stylistic; it brings check_jaxpr closer to
eval_jaxpr (and the other jaxpr interpreters) in organization. There's a
slight tweak to an error message which lets us save some slightly
redundant code.
* fixes and tweaks
revert find_top_trace change from #3197
The previous version was written and tested for performance; the revised
version caused at least a 25% slowdown in the dispatch time of
`lax.add(1, 2)` (and so likely a much bigger slowdown for the
find_top_trace timing alone).
Instead, we can just change the error message in xla.abstractify, since
invalid types lead to abstractification errors when we apply primitive
impls.
For a computation of the form:
>>> f = lambda x: x ** 2
>>> f = jax.jit(f)
>>> while run:
... x = f(x)
JAX must currently always have two copies of `x` in device memory since there
is no reliable way in Python to determine whether there will be future uses of
`x`. This causes two classes of problem:
1. Users at the limit of available device are constrained by the additional
copy of their parameters and other state while they typically only require
one copy. This typically frees 100M+ of device memory and is a critical
optimization for larger models to match state of the art performance in
other frameworks.
2. This constant alloc/free of the input/output buffers can cause memory
fragmentation on some platforms (although having a reusing allocator and
limiting run-ahead may be a better solution for this problem).
We propose fixing this by using input/output aliasing as supported by XLA. We
will support this in JAX by allowing certain arguments of jit/pmap decorated
functions to be donated and reused as outputs:
>>> f = lambda x: x ** 2
>>> f = jit(f, donate_argnums=0)
>>> while run:
... x = f(x)
JAX will determine that the donated input `x` can alias with the output of the
function and it will instruct XLA it _must_ write the result to this buffer.
If a user tries to reuse a buffer after it has been donated they get an error
that the buffer is invalid:
>>> y = f(x)
>>> jax.device_get(x)
...
RuntimeError: Invalid argument: CopyToHostAsync() called on invalid buffer.
The semantics of `donate_argnums` follows that of `static_argnums`, namely that
it identifies positional arguments to the computation that are to be donated
to the computation and used as part of the output.
One feature that is also enabled by this is invalidating buffers that should
only be used once, for example PRNGKeys:
>>> @partial(jit, donate_argnums=0)
... def move(x):
... # Do something complex enough for JAX to just optimize it away.
... return tree_map(lambda x: x + x - x, x)
>>> def safe_eager_uniform(key, *a, **k):
... assert hasattr(key, 'device_buffer'), "random must run eagerly"
... key = move(key)
... return jax.random.uniform(key, *a, **k)
This is not a complete answer to random safety since it is still possible to
reuse a key as part of a traced computation, however it can be used to support
this feature (somewhat inefficiently) in eager mode.
* Added argument check to all primitives.
The issue that inspired this is that `lax.tie_in` is
easy to misuse if the first argument is not a JAX type, then
it silently disappears. This means that `lax.tie_in((x, x), const)`
is the same as `const` even though `x` is a tracer.
This error would be caught previously if core.skip_checks == False
because then `bind` checks its arguments. I have essentially added
an unconditional argument check to `bind`.
In case this is considered too inefficient, we can add argument
checking to individual primivites, e.g., tie_in. For most primitives
if a non-JAX array is passed, the `impl` rule would fire and `numpy`
would report the error somehow, perhaps.
* Merged find_top_trace with check_args
This was previously merged as #2948 but reverted awaiting the fixes
in some user code.
* Added argument check to all primitives.
The issue that inspired this is that `lax.tie_in` is
easy to misuse if the first argument is not a JAX type, then
it silently disappears. This means that `lax.tie_in((x, x), const)`
is the same as `const` even though `x` is a tracer.
This error would be caught previosuly if core.skip_checks == False
because then `bind` checks its arguments. I have essentially
added an unconditional argument check to `bind`.
In case this is considered too inefficient, we can add argument
checking to individual primivites, e.g., tie_in. For most primitives
if a non-JAX array is passed, the `impl` rule would fire and
`numpy` would report the error somehow, perhaps.
* Merged find_top_trace with check_args
