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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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[JAX] Error checking for mesh resource and update GemmPrimitive to use global_mesh_resource().fsdp_resource (#2088)

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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

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* remove unnecessary padding
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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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fp8 tests passing for E4M3, getting CUBLAS_STATUS_NOT_SUPPORTED for E5M2
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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

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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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* 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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* [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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* Add experimental Shardy support.

Production use is not yet recommended.

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* scaling enum abstract

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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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