GitLab

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

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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Co-authored-by: Hua Huang <huah@nvidia.com>
Co-authored-by: Ming Huang <mingh@nvidia.com>

* Fix NVTE_FRAMEWORK=all
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* fix
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* Fix
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* Workflow tests and fixes
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* Fix jax install
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* Fix
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* Update dep
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* Add numpy
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* Add dep
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* Update test_helper.py and add QuantizeConfig class for CurrentScaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Implement distributed current scaling by computing a global amax and scale before quantization
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

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

Introduce nvte_memset to provide a fill kernel that is faster than cudaMemsetAsync for small sizes (#1716)

* nvte_memset fills single float value
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Support larger sizes than a single value and add tests
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Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

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

* 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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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>

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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Co-authored-by: Hua Huang <huah@nvidia.com>
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Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* FFI for all softmax functions
Signed-off-by: Hua Huang <huah@nvidia.com>

* FFI for FusedAttnBackward and Dequantize

FusedAttnBackward passed all testes in test_fused_attn.py.
Dequantize is not used currently; finish it for completeness.
Signed-off-by: Hua Huang <huah@nvidia.com>

* Fix FusedAttnBackward FFI pybind & simplify
Signed-off-by: Hua Huang <huah@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci



* Revert changes to tests/jax/test_fused_attn.py
Signed-off-by: Hua Huang <huah@nvidia.com>

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Signed-off-by: Hua Huang <huah@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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[JAX] XLA Custom Calls with FFI for FusedAttnFwd, Quantize, Transpose, ActLuFP8, LayerNormForwardFP8FFI, and LayerNormBackwardFFI (#1263)

* Add TransposeFFI, test passed
Signed-off-by: Hua Huang <huah@nvidia.com>

* Add ActLuFP8FFI; fix TransposeFFI
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* Add QuantizeFFI
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* Add FusedAttnForwardFFI and some unit tests
Signed-off-by: Hua Huang <huah@nvidia.com>

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for more information, see https://pre-commit.ci



* Minor fix
Signed-off-by: Hua Huang <huah@nvidia.com>

* Add LayerNormForwardFP8FFI & LayerNormBackwardFFI
Signed-off-by: Hua Huang <huah@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Revise FusedAttnForwardFFI()
Signed-off-by: Hua Huang <huah@nvidia.com>

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for more information, see https://pre-commit.ci



* Add FFI_CudaGraph_Traits

All tests passed, ready for merge
Signed-off-by: Hua Huang <huah@nvidia.com>

* Bug fix for FFI data type mismatch

Also add a safeguard on the entrance to FFI function
Signed-off-by: Hua Huang <huah@nvidia.com>

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

* Specify python version
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* Add classifiers for python
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* Add utils to build wheels
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* make wheel scripts
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* Add aarch
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* Fixes
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* Fix paddle wheel
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* PaddlePaddle only builds for x86
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* Add optional fwk deps
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* Python3.8; catch install error
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* [wip] cudnn9 compile with paddle support
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* [wip] dont link cudnn
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* dlopen cudnn
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* fixes
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* fix
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* Fixes
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* fix
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* dynamically load nvrtc
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* Lint
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* remove residual packages; exclude stub from nvrtc .so search
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* Exclude builtins from nvrtc .so search
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* properly include files for sdist
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* paddle wheel tie to python version
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* Fix paddle build from src [wip]
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* Fix workflow paddle build
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* Fix paddle
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* Fix paddle
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* fix lint from pr986
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* fix
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* Add sanity wheel test
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* Add sanity import to wheel test
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* remove upper limit on paddlepaddle version
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* Remove unused imports
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* Remove pybind11 dependency
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* Fix cpp tests
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* Search .sos in cuda home
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* fixes
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* CLeanup, remove residual code
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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
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replaced plain C asserts with NVTE_CHECK to avoid unused-variable warnings
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* Apply formatting
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* Apply formatting
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* categorized `csrc/modules.cpp`
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* adapted the build tool
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---------
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