PyTorch/XLA 2.7 release

Highlights

• Easier training on Cloud TPUs with TorchPrime
• A new Pallas-based kernel for ragged paged attention, enabling further optimizations on vLLM TPU (#8791)
• Usability improvements
• Experimental JAX interoperability with JAX operations (#8781, #8789, #8830, #8878)
• re-enabled GPU CI build [#8593]

Stable Features

• Operator Lowering
  • Lower as_strided_copy to use fast path with slice (#8374)
  • Lower _conj_copy. (#8686)
• Support splitting physical axis in SPMD mesh (#8698)
• Support of placeholder tensor (#8785).
• Dynamo/AOTAutograd traceable flash attention(#8654)
• C++11 ABI build is the default

Experimental Features

• Gated Recurrent Unit (GRU) implemented with scan (#8777)
• Introduce apply_xla_patch_to_nn_linear to improve einsum performance (#8793)
• Enable default buffer donation for step barriers (#8721, #8982)

Usability

• Better profiling control: the start and the end of the profiling session can be controlled by the new profiler API (#8743)
• API to query number of cached compilation graphs (#8822)
• Enhancement on host-to-device transfer (#8849)

Bug fixes

• fix a bug in tensor.flatten (#8680)
• cummax: fix 0-sized dimension reduction. (#8653)
• Fix dk/dv autograd error on TPU flash attention (#8685)
• Fix a bug in flash attention where kv_seq_len should divide block_k_major. (#8671)
• [scan] Make sure inputs into fn are not device_data IR nodes(#8769)

Libtpu stable version

• Pin 2.7 release to stable libtpu version '0.0.11.1'

Deprecations

• Deprecate torch.export and instead, use torchax to export graph to StableHLO for full dynamism support
• Remove torch_xla.core.xla_model.xrt_world_size, replace with torch_xla.runtime.world_size
• Remove torch_xla.core.xla_model.get_ordinal, replace with torch_xla.runtime.global_ordinal
• Remove torch_xla.core.xla_model.parse_xla_device, replace with _utils.parse_xla_device
• Remove torch_xla.experimental.compile, replace with torch_xla.compile