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* Add primary weighs fp8 support for mxfp8
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* Fix unit test and add better error log to unit test
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* Move post all-gather processing out of for loop
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* Add descriptions and ASCII diagrams for partial cast and partial amax functions
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* Minor fix based on greptile bot
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* Fix compilation errors due to arch-specific PTX instructions
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* Remove unused noop flag from C API
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* Expose test_partial_cast
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* Skip mxfp8 partial cast test if mxfp8 is not available
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* Fix pytest error
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* pylint ignore unused manual_post_all_gather_processing
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* Fix error when using is_mxfp8_available
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* Initial API change
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* Change all imports and api
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* fix recipe tets
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* fix more tests
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* fix docs, tests, and make Jax change as well
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* Change internal uses of fp8_autocast
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* CG function, and small test fixes
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* Change instances of make_graphed_callables internally
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* Fix distributed tests
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* Review
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* Review
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* Fix test and add more docs
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* Make is_bf16_available public
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* fixes
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* Move multi tensors kernels from PyTorch extensions to core
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* Add int16 type to core (for storing fp32 param remainders)
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* Fix core build
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* same fix to scale
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* Fix perf, memory, vars
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* Fix junk output dtype for non-per tensor
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* Fixes for test and upgrade mcore version
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* Fix core tests
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---------
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* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
and 1x128 blocksize via setting block_scaling_dim == 2,1 respectively.

Scale tensors are stored in a format emenable for matrix multiplication,
however the integration of matmul is deferred as a separate story.

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of equivalents in the kitchen repository, where a subchannel recipe
has been used for end to end training.
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* MR feedback.
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* Change API name.
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* Fix merge conflict with name change.
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* Reuse scale calculation between quantizer refs.
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* Save memory by dropping reference to saved tensors.

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* Remove constexpr parameters from kernel.

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* Merge conflict from rebase.
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* Add shape implementations for block scaling.

nvte_shape was added upstream. Logic added
for block scaled fp8.
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* Move benchmark to te_playground
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* Remove amax_epsilon and pow_2_scales from tensor.

Hardcodes the default values.
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* Lint changes.
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* Safer ifdef in kernel.
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* Documentation prose.
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* Reuse compute_scale function from Current Scaling.
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* Bugfix on inf_value scale refactor.
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* Remove qopt calls from test.
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* Update pytest list.
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* Add copyright to reference scale calc.
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* Use ptx.cuh functions instead of cde.
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* Use TypeInfo
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* MR feedback.
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* Update CS scale update test to use updated ref impl
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* Update JAX scaling mode enum
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* Skip tests on Lovelace
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* Enable fp8_primary_weights for current scaling
Signed-off-by: kunlunl <kunlunl@nvidia.com>

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* Use different cast_master_weights_to_fp8 functions depending on the type of quantizer
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* 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 test_replace_raw_data into qa/L0_pytorch_unittest/test.sh
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* Minor changes in comments
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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 unimplemented function from header
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Cleanup
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