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assertion check
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>
Co-authored-by: Phuong Nguyen <phuonguyen@nvidia.com>

Fix code block in fp8_autocast docstring
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>
Co-authored-by: Phuong Nguyen <phuonguyen@nvidia.com>

* Rework shardy rules

* WAR for compound factor=1
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* Custom call tests passing
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix test_layer.py
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* Lint
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* Fix comments
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* Support using amax on HighPrecision tensor if it exists instead of recomputing for current scaling
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* Fix shardy issue with amax being shape 1,1,1 instead of shape (1,)
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* Add higher-precision VJP tests to test_distributed_layernorm_mlp
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* Cast non-quantized kernels to input dtype in VJPs
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* Rename HighPrecisionTensor to NoScaleTensor
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* Use NoScaleTensor in pure JAX impls where it was missing
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* Fix tests
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* 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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* added amax as an optional arg
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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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* Support setting collection name for quantizer set Flax variables in TransformerEngineBase flax module
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* Support creating quantizer set from a recipe directly
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* Fix debug error format string in gemm.py
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* Lint
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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
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* remove unnecessary padding
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* adapt the test_distributed_layernorm byte count
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>


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* added XLA FFI custom op for TE/common nvte_cublas_gemm
Signed-off-by: Alp Dener <adener@nvidia.com>

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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* added sequence-first input warnings
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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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* Fix JAX grouped gemm error on CUDA 12.9.1 & later by using 16B alignment for scale ptr
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* Pad MXFP8 scales with 2*-127 instead of NaNs
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---------
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* TensorUsage + FP8 GEMM with all layouts handling on BW
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>


---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* fixes for jittable grouped_quantize

* fixes for jittable grouped_gemm

* fix contracting_dim for wgrad gemm

* exclude jitted grouped_gemm from the unit test as it does not work cudaGraph

---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
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---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Co-authored-by: Hua Huang <huah@nvidia.com>

Revert "[JAX] GroupedDense v.2 without dynamic shape (#1721)"

This reverts commit 5d01ef21

.
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
Signed-off-by: Hua Huang <huah@nvidia.com>
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* refactor the multi_stream utils + implement nvte_multi_tensor_quantize in TE/Common

* implement GroupedQuantizer and grouped_quantize in jaxx

* fix logical_axes_names for transpose tensor in ScaledTensor
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---------
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* Fix L2 test_custom_call_compute.py L2 tests
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* Fix test_helper.py
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* Address comments
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* Update test_helper.py and add QuantizeConfig class for CurrentScaling
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* WIP distributed current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* 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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---------
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* Add experimental Shardy support.

Production use is not yet recommended.

---------
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* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* rm no scaling enum
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* update jax enum
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* rename QuantizeAxis to QuantizeLayout, get_layout to get_data_layout, q_axis to q_layout

* add fatten_axis option

* added gated act to test encoder

* sharding constraint fixes

* fix padding when flattening first dim needs to be padded

* update test sizes so that padding is tested

* rm output sharding as it can be done in the flax module

* sharding scale_inv for mxfp8

---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* 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.

Fusions of quantization and DBIAS or activation functions are not yet
implemented, and the dequantization is currently implemented in torch.

Tests for quantization are included in C++ and pytorch layers, with
exact comparison to reference quantizer behavior as well as an attempt
to hit interesting branches through the API such as tensor creation
in pytorch and CPP and dequantization of row and columnwise usage.

Two CUDA kernels for quantization are included, and are direct ports
of equivalents in the kitchen repository, where a subchannel recipe
has been used for end to end training.
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* Apply linting changes.
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* Alignment for 1D scaling for GEMM edge case.
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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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* Use common tensor map API.
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* Change API to use two scaling mode enums.
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* Fix typo.
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* Update some call sites.
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* Tests for torch tensor API surface.

Since the quantized tensor is a tensor
subclass, these tests exercise torch hooks.
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* Reuse scale calculation between quantizer refs.
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* Save memory by dropping reference to saved tensors.

Issues previously observed are solved.
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* Remove constexpr parameters from kernel.

Code size is reduced with fewer constexpr params.
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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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* Fixup MR changes that broke.
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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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* Update shape logic with allocation and reuse shape.
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* Usage defaults MR feedback.
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* Copyright and header guard.
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* Updating torch dispatch code.
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* Fix exception type.
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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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* 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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