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Update copyright to include 2026
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Refactor to avoid storing a global quantization config so direct recipe passing works as intended
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* fix use_split_accumulator for current scaling recipe
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* fix tests that pass direct recipe and were missing quantize meta set
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* Revert "fix use_split_accumulator for current scaling recipe"

This reverts commit a74ab7df812ec0a069b1bdd208debb93ec25a900.
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* fix ci failures
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* Fix amax_history post_init
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* Update transformer_engine/jax/quantize/quantizer.py
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* fix ci failures
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* fix ci issue
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* address comments
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* make recipe assertion classes in test_recipe_characteristics not inherit from unittest.TestCase
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* Make SR rng state always 2D (num_devices, 4)
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* fix pure-jax impl
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* fix test shape
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* [JAX] Support recipe flags for disabling SR, RHT, and 2D quantization
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* lint
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* Fix issue with SR state being erased due to pytree handling of NVFP4Quantizer
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* Add test for SR state preservation across VJP boundaries
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* Fix sharding of SR rng state
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* lint
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* update tolerances slightly now that SR is enabled
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* lint
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* Use hashlib for deterministic hashes across runs for SR
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* rename uses_rht on scaled tensors to has_applied_rht
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* add assert
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* Move decision of whether to use RHT into helper.py and add dedicated RHT tests
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* lint
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* fix use_rht attr usage
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* fix pure-jax rht usage criteria
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* Adjust tolerances after rebase
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* Initial API change
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* Change all imports and api
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* format
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* fixes
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* fix typo
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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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* Address nits
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* rename file
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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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* Cleanup test imports and minimize internal file imports
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* Make is_bf16_available public
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* fixes
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* fix tests
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* Better docs and better api
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* format
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* Apply suggestions from code review
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* fix nvfp4 test
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Fix code block in fp8_autocast docstring
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* Decouple recipe and scaling mode
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* Expose global QuantizeConfig instance as a getter
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* Format and lint
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* Merge branch 'main' into dev/jberchtold/jax-scaling-mode-and-recipe-decoupling
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* Rename UsageType to TensorSource
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* Update test_layer.py
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[JAX] Error checking for mesh resource and update GemmPrimitive to use global_mesh_resource().fsdp_resource (#2088)

* Enforce global MeshResource is set
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* Use global_mesh_resource().fsdp_resource in gemm primitive
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* Update tests
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* Update gemm.py
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* Update test_layer.py
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* enabled TE GEMM for all recipes
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* add warnings
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* fix lint
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* add manage_primitives() helper

* disable GEMM primitives for non-MXFP8 recipes

* implement the NVTE_JAX_CUSTOM_CALLS + deprecate NVTE_JAX_CUSTOM_CALLS_RE

* replace NVTE_JAX_CUSTOM_CALLS_RE with NVTE_JAX_CUSTOM_CALLS in TE tests and examples

* fix use_jax_gemm contextmanager
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* added XLA FFI custom op for TE/common nvte_cublas_gemm
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started GemmPrimitive, abstract done
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gemm custom op working with BF16, needs testing for FP8/MXFP8
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converted TE GEMM API to use ScaledTensor and added os ENV flag to use TE GEMM under general gemm() call
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BF16 tests passing, FP8 tests should be passing but contracting_dims has a scoping issue
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fp8 tests passing for E4M3, getting CUBLAS_STATUS_NOT_SUPPORTED for E5M2
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updated GEMM API to use separate LHS and RHS quantizers instead of a QuantizerSet
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new GemmPrimitive passing all Dense tests
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import cleanup and reverted code chunk movement
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removed unused .transpose() implementations from ScaledTensors
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all custom call tests passing on Hopper, GEMM-related tests cover both GemmPrimitive and native JAX impl
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removed direct calls to GemmPrimitive.enabled() from outside of cpp_extensions
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removed unused changes to ScaledTensor classes and debug prints
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* minor unit test cleanup
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* FP8 tests passing on Blackwell but MXFP8 outputs NaN
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* reverted dense and fuseddense changes, FP8 test passing on Hopper and Blackwell, MXFP8 has issues with E5M2
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* MXFP8 issue traced to scale factor padding with NaNs instead of zeros
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* padding scale with 2^-127 instead of nans
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* fix bug on rhs_scale_inv usage
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* cleanup E8M0 type converter use it in gemm.cpp
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* segfault fixed, passing all unittests on Blackwell
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* fix for fuseddense tests
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* fix workspace alignment
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* fixed GemmPrimitive custom partitioning to match jax.nn.scaled_matmul
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all unit tests passing on H100x8 node
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linting fixes
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fixed batch dimension numbers
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fixed FP8 scale sharding rule when there are no FP8 scales
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added error message for unsupported Shardy partitioner
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fixed test tolerances for FP8 cases
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fixed shardy test skip cases
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* moved reshape of encoder output in encoder examples to make custom partitioning rules work correctly
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* added helper functions for padding and unpadding block scales, changed GemmPrimitive to accept unpadded scales and pad them after sharding
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* updated shardy rules for all custom ops to decouple block scale rules from their tensors
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* fixed linting errors
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* changed unit test use_jax_gemm option to be a context to preserve external custom op settings, tightened multi-GPU encoder test tolerances, changed gemm() API to use contracting_dims and batched_dims separately instead of dimension_numbers
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* fixed typo in test utils
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* fixed datasets version for JAX examples
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* reverting modification to force_1x_quantization decision
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* corrected gemm function syntax in unit tests
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* TensorUsage + FP8 GEMM with all layouts handling on BW
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---------
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* Update test_helper.py and add QuantizeConfig class for CurrentScaling
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* WIP distributed current scaling
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* Distributed Current Scaling (debugging). Distributed implementation with replicated scale_inv works for layernorm_mlp but feels like a hack

Has different per-device scale_inv values, but jax.debug.print only shows one of them. Since we're telling JAX/XLA that this scale is replicated, I think it assumes all the values are equal. However, it doesn't actually check this, so it seems we are able to get away with per-device scales for current scaling but I am not sure how stable this will be and may randomly fail if us or the user changes partitioning at all or if XLA decides to actually act on the assumption that all these scale_invs are the same.
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* Implement distributed current scaling by computing a global amax and scale before quantization
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* Add encoder and mnist tests for current scaling
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* Add primitive prefix to shardy unique_vars to prevent factor conflicts when performing unfused primitives for current scaling
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* Remove scale_shape primitive arg that is no longer used
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* Format
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* Fix expected result on multiprocessing encoder test
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* Lint fix
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* Update multiprocessing current scaling tolerances
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* Uncomment test case that was disabled for testing
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* Remove commented out debug line
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* [JAX-Q] Single GPU current scaling for JAX
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* Fix scale check dtype for MXFP8 scales affecting tests using assert_bitwise_scaled_tensors
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* Address comments
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* Remove cast to fp32 for norm primitives now that zero-centered gamma dtype issue is fixed
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* Fix lint issue
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* Remove unnecessary cast to fp32
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* Lint
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* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

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* expose NVTE_FP8_COLLECTION_NAME, update_collections, get_delayed_scaling

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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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