Cache HLO in xb.call_jax and support non-tensor args#8878
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The main purpose is to replace the clunky manual XlaComputation object caching at https://github.com/AI-Hypercomputer/torchprime/blob/b0bd47e3c732c56e75d8d2b315f05e06d485dd22/torchprime/torch_xla_models/experimental/custom_kernel.py#L16, and just write `xb.call_jax(some_jax_func)` and simply avoid repeated tracing there. We can't reuse the tracing cache in `jax.jit` because we jit a wrapper and not `jax_func`. Also `as_serialized_hlo_module_proto` has overhead itself and it would be nice to avoid calling that repeatedly. Also we improve `xb.call_jax` to support non-tensor arguments. These arguments are passed from `xb.call_jax` to the JAX function unchanged. They are considered "static arguments" and will be baked into the HLO. Because they are considered static args, we'll re-trace the jax function whenever their values change. Fixes #8795.
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The main purpose is to replace the clunky manual XlaComputation object caching at
https://github.com/AI-Hypercomputer/torchprime/blob/b0bd47e3c732c56e75d8d2b315f05e06d485dd22/torchprime/torch_xla_models/experimental/custom_kernel.py#L16, and just write
xb.call_jax(some_jax_func)and simply avoid repeated tracing there.We can't reuse the tracing cache in
jax.jitbecause we jit a wrapper and notjax_func. Alsoas_serialized_hlo_module_protohas overhead itself and it would be nice to avoid calling that repeatedly.Also we improve
xb.call_jaxto support non-tensor arguments. These arguments are passed fromxb.call_jaxto the JAX function unchanged. They are considered "static arguments" and will be baked into the HLO.Because they are considered static args, we'll re-trace the jax function whenever their values change.
Fixes #8795.