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* fix
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* fix
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* subtitle --- fix in many files:
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* cross entropy _input -> input rename
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* cross entropy _input -> input rename
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* a lot of small fixes
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* torch_version() change
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* add missing module and fix warnings
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* fix
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* fix
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* fix
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* removed training whitespace:
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* Update docs/api/pytorch.rst
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* Fix import
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* Fix more imports
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* Fix NumPy docstring parameter spacing and indentation

- Standardize parameter documentation to use 'param : type' format (space before and after colon) per NumPy style guide
- Fix inconsistent indentation in cpu_offload.py docstring
- Modified 51 Python files across transformer_engine/pytorch
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* Make BSHD default for Unfused DPA, DPA and MHA in TE JAX
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* Remove explicit transpose_batch set for BSHD for DPA in JAX quickstart
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* Add warnings in DPA and MHA to warn users of change defaults to BSHD instead of SBHD
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* Minimize the scope of when to trigger warnings for changed defaults for transpose_batch_sequence
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* jax quickstart guide first commit
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* edit the syntax errors and remove unnecessary comments in utils. Add some footnotes in the quick start notebook
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* Fix greptiles comments on spelling, deepcopy, vjp function signature comaptibility with speedometer
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* Add Copyright to utils and fix some more greptiles complaints
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* Add comments to alternative of layers
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* Remove weight sharing between different iterations of the transformerLayer
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* Add enum for attention implementations. Fix inconsistency between fuse and unfused TE impls to achieve same performance (removing extra dropout layer in fused layers. Also some minor wording changes
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* Fix bug in TransformerLayer expected input shape being [sequence, batch, ...] instead of [batch, sequence,...]
Signed-off-by: tdophung <tdophung@nvidia.com>

* Changing structure of notebook to  bring fp8 ahead of fuse, to allow for fuse to take effect because quantization exist as suggested. Also make TransformerLayer perf get closer to Fused by setting hidden_dropout=0
Signed-off-by: tdophung <tdophung@nvidia.com>

* add option to choose between different attention implementation in call of BasicTETransformerLayer and demonstrated difference in runtime between using flax and using te's attetion implementation
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* Fix mistake in lacking attention_implementation in FuseTETransformerLayer
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* Removing AttentionWrapper and custom built DPA, using flax and TE's impl only, removing last mention of Pytorch
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* More changing to markdowns to remove pytorch
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* cosmetics fixes
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* changing names of all implementations
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* change fp8_autocast to autocast, make causal mask, and some wording changes
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