GitLab

* [JAX] Support recipe flags for disabling SR, RHT, and 2D quantization
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* 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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* Improve error message for cublas fp8 gemm with incorrect shape
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* lint
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* Removed unnecessary non-contracting size check
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* rename inner dim -> leading dim
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* Try async copy of grouped GEMM group_sizes data
Signed-off-by: Hua Huang <huah@nvidia.com>

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* not fuse bias for output all reduction case + unit tests
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* norm to reduce dgamma along tpsp as well
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* clean up tests
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* fix test_distributed_layernorm byte counts
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* increase tols for jax_gemm
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* init cgemm + unit tests

* UB bootstrap with NCCL, no MPI dependency

* add NVLINK-P2P check + error message

* skip tests if no NVLINK available

* use std::vector to store ncclComm_t

* update misuse of TP warning
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* Rework shardy rules

* WAR for compound factor=1
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* add swizzle in jax
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* added outer_impl
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* clean up FFI
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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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* 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
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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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* clean up sharding
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* added tpsp_resource
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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
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* 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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* 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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* 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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* 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 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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* moved reshape of encoder output in encoder examples to make custom partitioning rules work correctly
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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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* 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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* 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 
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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 
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* fix otype for fp8 gemm
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* 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
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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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* Format
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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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* Fix lint issue
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* Remove unnecessary cast to fp32
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* Lint
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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

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* fix norm sharding

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* rm no scaling enum
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* refactor + mxfp8

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