GitLab

* Fix clearing tensor data in backward removing is_first_op
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Misc fixes
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Use Linear weight dtype and device for compute consistently
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Add backward dbias + quantize fusion
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Pass recipe to OperationFuser to allow recipe-dependent fusions
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Remove redundant view from activations
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* Add bias activation backward fusion
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* Change pre_forward to pre_first_forward
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* Fix passing invalid recipe with fp8 disabled
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---------
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* Replace `is_float8_tensor` with `is_quantized_tensor`

Replace free function `is_float8_tensor` with `is_quantized_tensor` in `_common.py` and use it throughout the `ops` codebase to check if a tensor is a (possibly internal) quantized tensor
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Pass next and previous op quantizers directly to op_forward and fuser_forward

Change interface of `fuser_forward` and `op_forward` to no longer take preceding and following ops and instead take the following op's input quantizer and preceding op's input gradient's quantizer directly
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Remove use redundant `detach` in `BasicLinear`

Remove use of `detach` in `BasicLinear` for improved performance (enabled by not passing prev_op to backward)
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* Handle saving internal tensors

Handle saving internal tensors in `_OperationFuserAutogradFunction` using `prepare_for_saving` and `restore_from_saved`
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* Use internal tensors

Enable use of internal tensors in `BasicLinear` quantizers and fix issues resulting from internal tensors not having methods that regular tensors have
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* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
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* Update NVTE_BLOCK_SCALING for GEMM.
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* Gate feature on CUDA 12.9
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* Gemm typo.
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* Remove unecessary type converter change.
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* Reflect epilogue availability and test supported epilogues.
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* GEMM simplifications from recipe branch.
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* Force pow2Scales in GEMM
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* Update FIXME to TODO with linked issue.
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* Update full recompute feature to save recipe.

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* MR Feedback. Avoid reusing quantizer objects.
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* Update for PP bug.
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* Update force_power_of_2 scales in the recipe.
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* Update usage method to satisfy upstream changes.
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* fix subchannel recipe in distributed test with bf16 gather
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* Edit and cleanup BF16 gather code.
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* Update test import.
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* Document and type code.
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* Update pytorch lint errors.
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* Dont set high precision dtype.
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* Add test for sanity and CG; fix CG for sequential?
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* Keep make_quantizers API stable

Update num_quantizers instead to pass cuda_graph tests.
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* Fix import name.
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* Rename recipe method.
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* Skip grouped linear sanity test.
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* Set usage before BF16 gather.
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* refactor for nvte_quantize_v2
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* Format code.
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* Cleanup nvte_quantize_v2
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* Test fp32 scales.
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* Disable CUDA graph.
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* Simplify layernorm linear
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* Cleanup layernorm linear.
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* LayerNorm linear bwd gather logic.
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* Communication updates.
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* Update transformer_engine/pytorch/ops/op.py

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* Lint fix.
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* MR feedback.
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* Enable cuda graph tests.
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* Reduce chance of spurious failure and reword.
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* Review suggestions from @timmoon10
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* Update CPP tests.
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* Update common.h
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* Update test_float8blockwisetensor.py
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* Cleanup sanity tests and add CS recipe tests
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* Fix sanity test
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* Forward args in pre_forward func to base op class
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Signed-off-by: Tim Moon <tmoon@nvidia.com>

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

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



* Review suggestions from @sudhakarsingh27 and @ksivaman

Fix spelling of "fusible". Avoid "input" name in internal APIs.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Update transformer_engine/pytorch/ops/__init__.py
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

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Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>