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Fix FP8 activation recompute
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Upgrade pylint and first round formatting
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* round 2
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* round 3
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* Format and fixes
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* Paddle lint
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* Reviews
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* FIxes
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* More linting
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* Fixes
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---------
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* CPU perf optimization in linear autograd function

Avoid enable_grad context when possible in cast function. Cache distributed group properties.
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* CPU perf optimization in prepare_forward function

Avoid torch.nn.Module impl of __setattr__.
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* Avoid module import in TE module forwards
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* Use fast getter for params
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* Reuse tensor dims in linear autograd func
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* Apply optimizations to grouped linear
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* Debug test failures
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* Fix linter warnings
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* Avoid deepcopy in tests
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* Move _fast_setattr logic to __setattr__ method
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* Removing the unused options from GroupedLinear docs and fixing the bug
with offsets
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* offsets -> fp8_meta_offsets
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* Docs fixes
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* docs fix
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* docs fix
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* Check if network interface name is valid and show useful warning message when initializing Userbuffers
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* Fix formatting issue in warning message.
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* Add base class for tensor proxies
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* Include transpose caching logic in proxy encode function
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* Debug dimension mismatch with amax history
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* Move dequantize logic to proxy_decode func
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* Rename to "QuantizedTensor"
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* Rename "proxy_detach" to "detach"
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* Include transpose cache in detach and clone funcs
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* Fix linter warnings
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* Update FP8 workspaces with QuantizedTensor functions
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* Move logic for FP8 transpose cache in FP8 workspaces to base class
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* Remove cast-transpose logic from linear op
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* Remove unnecessary args for Float8Tensor when using FP8 attr dict
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* Fix linter warnings
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* Update tests/pytorch/test_float8tensor.py
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* Debug FP8 transpose test
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* Debug cast functions
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* propagate scale_inv modification to GroupedLinear
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* optimization for separate scale_inv of weights and single output
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* let grouped gemm support different input combinations
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* fix type
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* add contiguous check
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* use len() instead of isinstance
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* fix ut
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* [TE/PyTorch][MoE] Add FP8 padding and unpadding module 

 1. Add multi-tensor padding kernel for FP8 with padding size = 16.
 2. Add FP8Padding and Fp8Unpadding module
 3. Add Padded GroupedLinear unit tests

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* fp8 mha with rope
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* avoid index select in cast ops
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* avoid index select in fused_attn_fwd
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* rename is_first_module_in_mha to fp8_output
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* resolve comments
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* resolve comments
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* move transpose to backward for fp8 input
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* fix ut
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* resolve comments
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* update argument list for CP
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* fix for FA3
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* remove unnecessary copy of scale_inv
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* skip fp8 dpa/mha tests when fa3 is not available
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* fix a merge bug
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* Perform scale-inv update in cast-transpose kernels
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* Perform scale-inv update in cast and activation kernels
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* Perform sclae-inv update in LayerNorm and RMSNorm kernels
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* Perform scale-inv update after FP8 GEMMs
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* Fuse casts and scale-inv updates in layernorm-linear module
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* Simplify kernel to update FP8 scale-inv
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* Fix typos
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* Debug amax update in layernorm kernels
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* Debug test failures
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feat(pytorch): Allow TransformerLayer and MultiheadAttention to accept sequence length parameters (#1066)

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* Add tests for THD layout, assert for THD layout with KV-Cache
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* Move THD logic in shape calculation, add missing optional in params
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* DGRAD-RS overlap bug fix

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* Bug fix for ring_exchange ReduceScatter

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* use fused_multi_cast_transpose
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* fix input being empty tensor
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* allocate output tensors in C++
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[C/PyTorch] Fixed incorrect use of `torch.distributed.new_group()` when creating intra-node group in `initialize_ub()` (#1087)

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* fix logging in attention
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* remove redundant imports
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* fix workspaces and unfused bias in multi-stream cuBLAS

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* Fix C-compatibility

* rm importing packaging in test_fused_attn.py

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* Ensure that the inputs to custom calls are contiguous
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[PyTorch] Fixing hang in `initialize_ub()` for multi-node runs after PR901 removal of MPI-dependence (#986)

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* fixed multi-node hang in initialize_ub(), updated comm+GEMM overlap example to support multi-node mixed tensor/data parallelism, added README
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* double-free fix in userbuffers destructor
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* removed unnecessary and incorrect torch.cuda.set_device(...)
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* corrected inter-node ranks logic
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* added single-node comm+GEMM overlap unit tests
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* LayerNormMLP example confirmed working with 2 nodes on Eos
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* unit test cleanup
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* applied RS overlap FP8 fix from PR1004
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* Fixed convergence issues
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* remove implicit padding and unpadding
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