GitLab

[QA] Add error handling

- Standardize test failure handling using the unified 'test_fail' function and 'error_exit' function
Signed-off-by: Linxi Ding <linxid@nvidia.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

* Add option to cache activation input in FP8
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Avoid casting to FP8 transpose
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Skip input caching if device is not supported
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Add documentation that FP8 input caching is experimental
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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Signed-off-by: Tim Moon <tmoon@nvidia.com>

Repeated calls to nvte_shape should not invalidate
previous data pointers.

It would be possible to avoid unnecessary comparisons
by duplicating some of the logic from shape() so that
the cache is only relevant when columnwise shapes are
involved. Whether this code duplication is preferable
to the comparisons that arise from by value semantics
of reusing shape is a judgment call.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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

* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update NVTE_BLOCK_SCALING for GEMM.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Gate feature on CUDA 12.9
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Gemm typo.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Remove unecessary type converter change.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Reflect epilogue availability and test supported epilogues.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* GEMM simplifications from recipe branch.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Format py code.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update GEMM DGelu tests to match support depending on output dtype.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Force pow2Scales in GEMM
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add GEMM test to pytorch test suite.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add copyright to GEMM test.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update import for GEMM test.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add license.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update test gemm supported predicate.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Use sgemm like interfaces and naming.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Rewrite GEMM comment.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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

* Recipe setup for Linear modules.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Use 12.9 feature test.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Run against tensor dumps from internal library.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update FIXME to TODO with linked issue.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update full recompute feature to save recipe.

The recompute context uses the same recipe
and fp8 settings as the original fwd pass.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* MR Feedback. Avoid reusing quantizer objects.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update logic in module.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Format py.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update for PP bug.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update test numerics.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update force_power_of_2 scales in the recipe.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update usage method to satisfy upstream changes.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* fix subchannel recipe in distributed test with bf16 gather
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* Edit and cleanup BF16 gather code.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update test import.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* support columnwise only mode to 1D quantize kernel
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* Format and move enum
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Skip alloc.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* try async bf16 gather
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* Format python code.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Document and type code.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update pytorch lint errors.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Dont set high precision dtype.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add test for sanity and CG; fix CG for sequential?
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Keep make_quantizers API stable

Update num_quantizers instead to pass cuda_graph tests.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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

* Rename recipe method.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Skip grouped linear sanity test.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Set usage before BF16 gather.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* refactor for nvte_quantize_v2
Signed-off-by: zhongboz <zhongboz@nvidia.com>

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

* Cleanup nvte_quantize_v2
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Test fp32 scales.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Disable CUDA graph.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Simplify layernorm linear
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Cleanup layernorm linear.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* LayerNorm linear bwd gather logic.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Communication updates.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update transformer_engine/pytorch/ops/op.py

Apply MR comment change.
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: kwyss-nvidia <kwyss@nvidia.com>

* Lint fix.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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

* Enable cuda graph tests.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Reduce chance of spurious failure and reword.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Review suggestions from @timmoon10
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Update CPP tests.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update common.h
Signed-off-by: Xin Yao <yaox12@outlook.com>

* Update test_float8blockwisetensor.py
Signed-off-by: Xin Yao <yaox12@outlook.com>

---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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* Debug checkpointing with te.Sequential
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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Explicitly specify quantized tensor usages needed for linear op backward
Signed-off-by: Tim Moon <tmoon@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>

* Minor stylistic tweaks and typo fixes

Review suggestions from @ptrendx
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Fix incorrect col strides for MXFP8 matrices
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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* Use dummy wgrads for lower memory consumption
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Vasudevan Rengasamy <vrengasamy@nvidia.com>

* Bug fix to avoid sharing gradients.
Signed-off-by: Vasudevan Rengasamy <vrengasamy@nvidia.com>

* Disable automatic use of batch_p2p_comm for CP2
Signed-off-by: Vasudevan Rengasamy <vrengasamy@nvidia.com>

* Change weight to origin_weight for LN_LINEAR
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* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update NVTE_BLOCK_SCALING for GEMM.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Gate feature on CUDA 12.9
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Gemm typo.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Remove unecessary type converter change.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Reflect epilogue availability and test supported epilogues.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* GEMM simplifications from recipe branch.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Format py code.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update GEMM DGelu tests to match support depending on output dtype.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Force pow2Scales in GEMM
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add GEMM test to pytorch test suite.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add copyright to GEMM test.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update import for GEMM test.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add license.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update test gemm supported predicate.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Use sgemm like interfaces and naming.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Rewrite GEMM comment.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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

* Refactor GEMM param canonicalization

Configure A and B matrices separately. Have separate code path for each scaling mode.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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* Prune number of tests.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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* rm no scaling enum
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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

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

Support fp8 primary weight in fsdp training
Signed-off-by: jianbinc <shjwudp@gmail.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* fix cpp warning
Signed-off-by: Xin Yao <xiny@nvidia.com>

* more fix
Signed-off-by: Xin Yao <xiny@nvidia.com>

---------
Signed-off-by: Xin Yao <xiny@nvidia.com>

* refactor to add cp support for sbhd/bshd
Signed-off-by: Xin Yao <xiny@nvidia.com>

* support interleaved
Signed-off-by: Xin Yao <xiny@nvidia.com>

* format
Signed-off-by: Xin Yao <xiny@nvidia.com>

* add interleaved to RotaryPositionEmbedding in test
Signed-off-by: Xin Yao <xiny@nvidia.com>

* update
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* merge sbhd/bshd and thd functions
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---------
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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

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

MXFP8 flax layer tests
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
and 1x128 blocksize via setting block_scaling_dim == 2,1 respectively.

Scale tensors are stored in a format emenable for matrix multiplication,
however the integration of matmul is deferred as a separate story.

Fusions of quantization and DBIAS or activation functions are not yet
implemented, and the dequantization is currently implemented in torch.

Tests for quantization are included in C++ and pytorch layers, with
exact comparison to reference quantizer behavior as well as an attempt
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
has been used for end to end training.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Apply linting changes.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Alignment for 1D scaling for GEMM edge case.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* MR feedback.
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* Change API name.
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* Fix merge conflict with name change.
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* Use common tensor map API.
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* Change API to use two scaling mode enums.
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* Fix typo.
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* Update some call sites.
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* Tests for torch tensor API surface.

Since the quantized tensor is a tensor
subclass, these tests exercise torch hooks.
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* Reuse scale calculation between quantizer refs.
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* Save memory by dropping reference to saved tensors.

Issues previously observed are solved.
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* Remove constexpr parameters from kernel.

Code size is reduced with fewer constexpr params.
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* Merge conflict from rebase.
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* Add shape implementations for block scaling.

nvte_shape was added upstream. Logic added
for block scaled fp8.
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* Move benchmark to te_playground
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* Remove amax_epsilon and pow_2_scales from tensor.

Hardcodes the default values.
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* Lint changes.
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* Fixup MR changes that broke.
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* Safer ifdef in kernel.
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* Documentation prose.
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* Reuse compute_scale function from Current Scaling.
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* Bugfix on inf_value scale refactor.
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* Remove qopt calls from test.
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* Update pytest list.
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* Add copyright to reference scale calc.
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* Use ptx.cuh functions instead of cde.
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* Update shape logic with allocation and reuse shape.
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* Usage defaults MR feedback.
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* Copyright and header guard.
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* Updating torch dispatch code.
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* Fix exception type.
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* Use TypeInfo
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* MR feedback.
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* Update CS scale update test to use updated ref impl
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* Update JAX scaling mode enum
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* Skip tests on Lovelace
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Make sure that weight matrix has required usages for dgrad GEMM
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* add nvshmem based api support
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* fix lint and license issue
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* remove asset
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Update list of authorized users
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* Fix fp8_buf for Linear and LayerNormLinear
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* Fix
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* expose NVTE_FP8_COLLECTION_NAME, update_collections, get_delayed_scaling

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* Overlap input all-gather with dgrad GEMM in FP8 linear layers
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* Fix GEMM+RS overlap for LayerNormMLP
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* Update dtype for wgrad GEMM
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* [PyTorch] fix general_gemm argument out_dtype in LayerNormMLP backward
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* refactor + mxfp8

* added grouped gemm

* rename linear to dense

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* relax the tol of test encoder multiprocessing mxfp8 by 0.001
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* Fix linter warning
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