GitLab

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

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

* update tests
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* rework test for MeshResource
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* add mesh_resource into fp8_autocast in test_helper.py
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* added shardy warning
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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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* fix fsdp
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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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* rm batch_dim, sequence_dim, sequence_parallel_output
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* rm lhs_quantized_colwise and rhs_quantized_colwise
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* rm unnecessary transpose_batch_sequence arg from some modules
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* new gemm operand specs processing
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* fix for lhs_non_specs
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[JAX] Fixing GemmPrimitive partitioning rules to handle tensor-parallelism correctly for sequence-parallel inputs (#1980)

* updated GemmPrimitive partitioning rules to explicitly control all-reduce vs. reduce-scatter for sequence-parallelism
Signed-off-by: Alp Dener <adener@nvidia.com>

* corrected handling of FSDP sharding for the RHS operand
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* use correct logical axes variable to identify sequence-parallel dim in LayerNormDenseGeneral
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* fixed linting issues
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* added assert on sequence-parallel options when GemmPrimitive is disabled
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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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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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* TensorUsage + FP8 GEMM with all layouts handling on BW
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* Support MXFP8 and handle empty matrices
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* 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

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* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
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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>
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* fix otype for fp8 gemm
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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* removes unnecessary reshapes for FP8 GEMM

* use nn.jax.scaled_matmul
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* fp8 gemm with direct quant
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This reverts commit 5bee81e2

.
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* removes unneccessary reshapes for FP8 GEMM

* use nn.jax.scaled_matmul
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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* exclude GroupedGemm APIs
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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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---------
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* New GroupedGemmPrimitive using variadic args

* Remove squeeze() to reduce D2D memcpy

* Revert to the list append fashion to simplify code

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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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* 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

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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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