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[DCU] add env vars to suppress build warnings

See merge request dcutoolkit/deeplearing/TransformerEngine!2

add lightop rmsnorm

See merge request dcutoolkit/deeplearing/TransformerEngine!1

* Fix GEMM+RS overlap for LayerNormMLP
Signed-off-by: Guyue Huang <guyueh@nvidia.com>

* Fix error LayerNormMLP param.grad is None
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* Update dtype for wgrad GEMM
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* [PyTorch] fix general_gemm argument out_dtype in LayerNormMLP backward
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* refactor + mxfp8

* added grouped gemm

* rename linear to dense

* added cublas init phase for groupedGemm

* relax the tol of test encoder multiprocessing mxfp8 by 0.001
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* Handle case where FP8 current scaling quantizer gets default process group
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* Fix linter warning
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* Avoid canonicalizing TP group since it may not be initialized
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Add fast path for causal masking with segment IDs.
Signed-off-by: Michael Goldfarb <mgoldfarb@nvidia.com>

fix a race error softmax_lse
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* Cleanup sanity tests and add CS recipe tests
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* Fix sanity test
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* Fix CG capture with CS recipe
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* Fix ops for CG
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* Coalesce NCCL all-gathers for MXFP8 all-gather
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* Add missing import
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* Cache quantized input tensor after linear module forward pass
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* Fix linter warnings
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* Avoid unnecessarily allocating layernorm output in LayerNormLinear/LayerNormMLP
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* skip cuDNN 9.8 for KV caching
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* revert from max_seqlen_kv to max_sequence_length for InferenceParams
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* remove redundant None returns in bwd
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* add debug flags when no backend is found
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* skip kv_cache_accuracy tests for cuDNN 9.8
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* truncate length of cu_seqlens for consistency with q/k/v shape
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* fix cuDNN search dir
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* fixes based on review
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* remove extra empty line
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* Fix mxfp8 columnwise data missing when switching from validation to training
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* Fix when you interleave training and inference
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* refact
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* rm useless code
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* Update transformer_engine/pytorch/module/base.py
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* Fix linter warnings
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* Defer torch compilation steps until first function call
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* Fix function call in smoke test
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fix mcore DDP error
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import te before te_jax
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* Update usage of weightmat before saving for backward
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* Fix for layernorm mlp
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* fix dtypes of fused_attn_bwd in CP+A2A
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* fix dtypes of fused_attn_bwd in CP+P2P
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* fix amax_per_step
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* clone scaling factors of fwd quantizers
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* fix fwd quantizers of CP+P2P
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* minor change
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* dequantize fp8 out in CP unit test
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* delete redundant None in FusedAttnFunc bwd
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* Enable fp8_primary_weights for current scaling
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* Use different cast_master_weights_to_fp8 functions depending on the type of quantizer
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* All amaxes of model_weights should participate in reduce-max
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* Clear _high_precision_init_val automatically in cast_master_weights_to_fp8 function
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* Merge all all-reduce on amaxes into one NCCL kernel
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* Add unit tests for multi_tensor_compute_scale_and_scale_inv and preserve_high_precision_init_val
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* Fix conflicts
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* Add unit test for cast_master_weights_to_fp8
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* use mock group to initialize fp8_autocast to avoid reduction of amax_history by fp8_autocast_exit
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* Remove with_computing_amax and with_computing_scale
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* Move replace_raw_data from QuantizedTensor to utils.py
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* Remove allow_empty_output argument from nvte_compute_amax and set it always be true
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* Rename import guard of recipe_common.cuh to be align with other import guards
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* Add unit test for replace_raw_data
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* Add test_replace_raw_data into qa/L0_pytorch_unittest/test.sh
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* Minor changes in comments
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* Add randomness to the unit test of replace_raw_data
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* (Maybe need revert) Add tex.quantize_to_fragment
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* (Maybe needsto rrevert) Use nvte_quantize_noop in quantize_to_fragment
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* Fix lint error
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* Remove test_fp8_model_init.py and test_replace_raw_data.py
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* Remove cast_master_weights_to_fp8 and replace_raw_data from __all__ of tensor.__init__.py
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* Move FP8 casting logic back from C++ tex funcs to Python
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* Remove unimplemented function from header
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* Do not suppress MXFP8 norm in Python wrapper func
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* Support FP8 current scaling in tex norm functions
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* Debug compilation error
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* Fix compilation error
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* Fix full-tile requirement for MXFP8 norm kernels
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update cudnn-frontend to its new 1.11.0-rc
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