GitLab

* 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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* Review
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* Better docs and better api
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assertion check
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* Rework shardy rules

* WAR for compound factor=1
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* Custom call tests passing
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* Fix test_layer.py
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* Lint
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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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* Decouple recipe and scaling mode
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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)

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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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* Lint
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* enabled TE GEMM for all recipes
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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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* remove unnecessary padding
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* adapt the test_distributed_layernorm byte count
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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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all custom call tests passing on Hopper, GEMM-related tests cover both GemmPrimitive and native JAX impl
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removed unused changes to ScaledTensor classes and debug prints
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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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* segfault fixed, passing all unittests on Blackwell
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* fix for fuseddense tests
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all unit tests passing on H100x8 node
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linting fixes
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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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* fixed datasets version for JAX examples
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* reverting modification to force_1x_quantization decision
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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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* fixes for jittable grouped_quantize

* fixes for jittable grouped_gemm

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* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
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Revert "[JAX] GroupedDense v.2 without dynamic shape (#1721)"

This reverts commit 5d01ef21

.
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* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
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* refactor the multi_stream utils + implement nvte_multi_tensor_quantize in TE/Common

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* fix logical_axes_names for transpose tensor in ScaledTensor
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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
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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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* Remove scale_shape primitive arg that is no longer used
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* Format
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* Update multiprocessing current scaling tolerances
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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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* Remove cast to fp32 for norm primitives now that zero-centered gamma dtype issue is fixed
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* Add experimental Shardy support.

Production use is not yet recommended.

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* scaling enum abstract

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

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* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
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Scale tensors are stored in a format emenable for matrix multiplication,
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Fusions of quantization and DBIAS or activation functions are not yet
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Tests for quantization are included in C++ and pytorch layers, with
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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
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Hardcodes the default values.
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* Add copyright to reference scale calc.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Use ptx.cuh functions instead of cde.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update shape logic with allocation and reuse shape.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Usage defaults MR feedback.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Copyright and header guard.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Updating torch dispatch code.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Fix exception type.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Use TypeInfo
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* MR feedback.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update CS scale update test to use updated ref impl
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Update JAX scaling mode enum
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Skip tests on Lovelace
Signed-off-by: Tim Moon <tmoon@nvidia.com>

---------
Signed-off-by: Keith Wyss <kwyss@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Co-authored-by: Tim Moon <tmoon@nvidia.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

* expose NVTE_FP8_COLLECTION_NAME, update_collections, get_delayed_scaling

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

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

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