tensorflow changelog

Hey there, fabulous developers! 🌟 We've got some exciting updates and tweaks to share with you. Let's dive right into the latest changes:

New feature: 🚀 Add support for atomic_rmw fadd for bf16 on HOPPER

• Summary: This update brings in the magic of atomic_rmw fadd for bf16 data type on HOPPER CUDA compute capability within XLA:GPU and MLIR-based emitters. Now, you can perform atomic operations on bf16 data types with ease. A test case has been added to ensure everything runs smoothly on the HOPPER architecture.

Improvement: 🛠 Avoid building hlo_runner_main.cc twice

• Summary: We've streamlined the build process by moving the actual build into a shared library target and creating two binary targets that depend on it. This makes maintaining dependencies easier and more explicit. Say goodbye to redundant builds!

Improvement: 🏎️ Run fusion-wrapper pass before scheduling in XLA:GPU

• Summary: The fusion-wrapper pass now runs before scheduling in the GPU compiler. This change enhances the fusion and scheduling process, making it more efficient. Plus, there's a new test to ensure non-fused instructions are wrapped correctly.

New feature: 🌟 Open source XLA passes for Shardy

• Summary: Shardy just got a major upgrade with new XLA passes! We've added new files, headers, and functions for exporting and importing operations and shardings. Test files are also included to ensure everything works perfectly.

Improvement: ⚡️ Port concatenate instruction to Thunks in XLA:CPU

• Summary: Concatenate instructions are now ported to Thunks, with a fast concatenate option for better performance. Benchmarks show a 4% improvement in parallel concatenate performance and an 11% boost in CPU time. Fast concatenate without parallel processing shows a slight performance dip.

New feature: 🎉 Add a basic test case for circular pipeline collective permute

• Summary: A new test case for circular pipeline collective permute has been added. It involves a simple computation using collective permute with source-target pairs and verifies the results. A more complex test case is outlined for future implementation.

New feature: 🧸 Add a toy example for using Shardy

• Summary: A toy example for using Shardy in the XLA pipeline is now available. This includes changes to workspace files, BUILD files, a main file for Shardy optimization, and a test file with a simple MLIR test case. Perfect for getting started with Shardy!

New feature: 🔧 Add Thunk::ExecuteSession to control concurrent workers

• Summary: Control the number of concurrent workers processing XLA execute requests with Thunk::ExecuteSession. This helps manage task scheduling overheads for XLA programs with many tiny thunks. Unit tests ensure the locking mechanism works as expected.

Bugfix: 🐛 Remove support for CUDA versions below 12.3 in XLA

• Summary: We’ve streamlined XLA by removing support for CUDA versions below 12.3. This update affects multiple files related to GPU functionality, profiling, and testing, aligning XLA with the latest CUDA technology for improved performance.

Bugfix: 🛠 Revert fix for 3 DeadCode findings

• Summary: Reverted a previous fix that addressed 3 DeadCode findings related to DelayKernelIsSupported, LaunchDelayKernel, and UnsupportedGpuFeature. The revert undoes changes made to gpu_timer_kernel_rocm.cc and gpu_types.h.

Bugfix: ⚙️ Only use the kernel threadpool if it is enabled

• Summary: Added a conditional check to use the kernel threadpool only if it is enabled. This ensures optimal performance and resource utilization when working with TensorFlow Lite delegates.

Chore: 🧹 Make stablehlo tests private

• Summary: The visibility of stablehlo tests has been changed from public to private. This keeps these tests restricted to their intended scope, maintaining the integrity and organization of the codebase.

That's all for now, folks! Keep coding and stay awesome! ✨