Comments (2)
mattjj
commented on May 18, 2024
Thanks for raising this, and for starting the PRs #2, #3, and #4!
Those are all fixes we need to make, but it might take us some time to merge the PRs because we're still ironing out our code syncing process. In other words, we're not quite ready for PRs yet, but we'll try to merge these if and when we can.
Thanks for your interest and your work to improve JAX (even before we're ready for it!).
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cclauss
commented on May 18, 2024
No rush... Take the time that you need to get it right.
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Related Issues (20)
- jax.scipy.stats refactoring tests HOT 3
- Array `any` and `all` methods missing `where` parameter in type signature HOT 1
- Jax sharding API is too verbose HOT 4
- Concatenation error in `jax.numpy.diff` HOT 2
- jax-metal, M1: failed to materialize conversion for result #0 of operation 'mhlo.convolution'
- .at set unexpected behaviour HOT 7
- [better errors] passing batched key to `jax.random` leads to inscrutible error
- JAX NumPy array_split not throwing out of bounds error HOT 1
- `jax.jvp` is tricky to use with non-differentiable inputs. HOT 6
- jax.numpy.arcsinh not has accurate for complex64 dtype HOT 15
- Metal: "mhlo.pad" error when calling jax.numpy.cumsum HOT 1
- Proposal for generic mpi4py initialization of jax distributed module HOT 1
- using jnp.nozero is not possible in jax.eval_shape HOT 4
- Memory leak when allocating arrays in a for loop HOT 1
- Jax with cuda installation command not correct HOT 5
- Linear solvers (jax.numpy.linalg.solve, jax.scipy.linalg.solve) bugged when allocating large memory HOT 3
- jax.numpy.arange resists JIT even with static shape HOT 4
- BUG: `shard_alike` requires newer jaxlib
- Matrix multiplication inaccurate on A100 in both TF32 and FP32 HOT 3
- sm_scale in flash_attention_tpu does not work like what it should HOT 2
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