Implement the JAX transfer guard API

Adds `--jax_transfer_guard` flag and `jax.transfer_guard()` context manager that allows logging or disallowing unintended transfers.

The API distinguishes between two types of transfers:
* explicit transfers: `jax.device_put*()` and `jax.device_get()` calls.
* implicit transfers: Other transfers (e.g., printing a `DeviceArray`).

The transfer guard can take an action based on its guard level:

* "allow": Silently allow all transfers (default; same as the previous behavior).
* "log": Log and allow implicit transfers. Silently allow explicit transfers.
* "disallow": Disallow implicit transfers. Silently allow explicit transfers.
* "log_explicit": Log and allow all transfers.
* "disallow_explicit": Disallow all transfers.

The API also allows fine-control the transfer guard level of individual transfer directions. Their flag and context manager names are suffixed with the transfer direction:

* "host_to_device": Converting a Python value into a `DeviceBuffer`.
* "device_to_device": Copying a `DeviceBuffer` to a different device.
* "device_to_host": Fetching the value of a `DeviceBuffer`.

Example:
```
x = jnp.array(1)
y = jnp.array(2)
z = jnp.array(3)

print(x)  # No error
with jax.transfer_guard("disallow"):
  print(x)  # No error; x is already fetched
  print(jax.device_get(y))  # No error
  print(z)  # Error!
```

PiperOrigin-RevId: 427562278
Change-Id: I2617a795e67019632f03aaa511d686625c3d92cc
9 files changed
tree: 96770d59d13602f4d648673132f55e21fb8677d6
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .bazelrc
  6. .bazelversion
  7. .clang-format
  8. .gitignore
  9. .zenodo.json
  10. arm_compiler.BUILD
  11. AUTHORS
  12. BUILD
  13. CITATION.cff
  14. CODE_OF_CONDUCT.md
  15. CODEOWNERS
  16. configure
  17. configure.cmd
  18. configure.py
  19. CONTRIBUTING.md
  20. ISSUE_TEMPLATE.md
  21. ISSUES.md
  22. LICENSE
  23. models.BUILD
  24. README.md
  25. RELEASE.md
  26. SECURITY.md
  27. WORKSPACE
README.md

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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

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3
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