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Avoid registering FP8 recipe update in ops without backward pass
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* Register weight/bias params in linear op
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* Tweak docs
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* Make sure linear op checkpoint is backward-compatible
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* Fix linter warning
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* Check for invalid case before setting bias
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Fix bug when deducing dtype in linear functional API
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* Update amax pointers when reallocating amax history in fusible ops
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* Update weight tensor quantizer when recipe state is reset
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* fix: Add stream synchronization before destroying MPI communicator (#1979)
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* feat: Implement column-wise userbuffer overlap for comm+GEMM operations

Add support for overlapping column-wise allgather communication with GEMM
operations to improve training performance:

* **Core infrastructure changes:**
  - Update bulk_overlap_columnwise_ag() to accept explicit stream parameter
  - Modify userbuffers send/recv loops to use rank-ordered iteration
  - Add userbuffers_send_all/recv_all function declarations

* **Python integration:**
  - Add bulk_overlap_ag_with_external_gemm() C++ extension function
  - Expose new overlap function via pybind11 bindings
  - Update overlap method configurations to include more ring_exchange ops

* **LayerNorm MLP optimization:**
  - Enable column-wise quantization for FC2 gradient output
  - Implement overlap of allgather communication with FC2 DGRAD GEMM
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This optimization allows overlapping communication and computation phases
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* fix: Working userbuffer overlapping API
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* fix: Fix overwriting bulk overlap UB object for layernormLinear
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* fix: Update external overlap to use tp size instead of nvsize to determine number of copies
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* fix: Fix linter error
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* fix: Explanatory comments of overlap logic
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* fix: Fix the UB fused ops tests
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* fix: Fix linter errors
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Fix merge conflict bug with clearing op outputs
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* remove reciprocal op
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* Refactor Quantizer::create_tensor function
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* Fix bug when constructing FP8 tensor
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* Add quantize function to C++ quantizers
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* Prototype function to coerce Python quantized tensors to match quantizer
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* Use quantizer class in tex.quantize
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* Add FP8 current scaling support for activation backward
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* Disable quantized GEMM output with FP8 current scaling
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* Add coerce_tensor functions for MXFP8 and DSv3
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* Avoid quantizing empty tensors
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* Use consistent shapes for FP8 transposes
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* In attention impl, construct FP8 tensors with pre-initialized scale-invs
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* Initialize MXFP8 scales to zero
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* Store copy of quantizer when creating quantized tensors
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* Fix linter warnings
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* Make sure quantized tensors have private quantizer

Avoid problems with in-place ops after quantizer usages are changed externally.
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* Rename "coerce_tensor" to "convert_and_update_tensor"
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* Make sure CUDA context is available when launching NVRTC kernel
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* Expose CUDA context creation function externally
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* Add ops for dropout and constant scale
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* Mark output tensors as not deletable in backward
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* Increase intermediate precision and reuse tensors from fwd
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* JIT warmup only when required
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* Recompute only rsqrt_norm
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* Fix bug where TE ops were not updating fp8_meta dicts
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* Rename reset_recipe_state function
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* Update error message when initializing meta device quantized weight without recipe
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* Refactor _OperationFuserAutogradFunction.forward to use less parameters
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* Rename `BackwardBiasActivation` to `BackwardActivationBias`
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* Use forward operation order in backward fused operations
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* Rename `prev_op_grad_input_quantizer` to `prev_op_grad_output_quantizer`
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* Make OperationFuser persistent
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* Distribute extra inputs to and collect extra outputs from multiple module groups in Sequential
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* Take requires_grad into account when fusing operations
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* Change get_quantizer to return None if no quantization recipe is used
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* Refactor pre_first_forward
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* Fix fp8 meta tensors in CUDA Graph capture
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* Fix failing distributed userbuffers tests
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