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* 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
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* Add backward dbias + quantize fusion
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* Pass recipe to OperationFuser to allow recipe-dependent fusions
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* 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
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Fix passing invalid recipe with fp8 disabled
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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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* Apply suggestions from code review
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* Flatten basic op params during fuser init
Signed-off-by: Jan Bielak <jbielak@nvidia.com>
(cherry picked from commit 949abe97070721b1da5117903067608250f5fb61)

* Add caching for is_non_tn_fp8_gemm_supported
Signed-off-by: Jan Bielak <jbielak@nvidia.com>
(cherry picked from commit fd830ae24ffbd2d0727010b1a8a119ca72f61ce5)

* Pass fuser to _OperationFuserAutogradFunction.forward and moving computation to __init__
Signed-off-by: Jan Bielak <jbielak@nvidia.com>
(cherry picked from commit fd808991993958b670726896254b82fcb967fa07)

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* Pass basic_op_kwargs and is_grad_enabled as parameters rather than in fuser
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fix saved_tensors
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* Avoid `parameters` function in op backward pass
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* Handle deprecated `hidden_size` arg in norm modules
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* Support initializing norm ops on CPU
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* Add integration test for Megatron-LM
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* Rename Mcore integration test
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* Handle case in RMSNorm where hidden dim is not provided
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* Add Userbuffers support for column TP linear layer
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* Add Userbuffers support for row TP linear layer
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* Interpret linear+RS as row TP linear
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* Add Userbuffers support for FP8 row TP linear layer

Assumes FP8 RS, which is not a good assumption.
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* Debug bug with incorrect bias pointers in UB GEMM

Bias pointers are not properly offset for different data chunks. Also removed logic for FP8 RS.
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* Add Userbuffers support for linear dgrad

Test passes with row TP, fails with col TP.
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* Add Userbuffers support for linear wgrad
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* Add support for grad bias
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* Fused cast-transpose-dbias
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* Support case where wgrad is optional
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* Expand documentation
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* Fix linter warnings
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* Use recently added convenience functions in Float8Tensor
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* Respect autograd dtype
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* Fix missing imports
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* Respect PyT autocast dtype in bprop
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* Fix linter warnings
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* Debug merge conflicts
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* Add layer norm op
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* Add FP8 cast op
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* Add tests for linear and layernorm with FP8 output
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* RMSNorm op
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* Fix linter warnings
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* Replace LayerNorm module with LayerNorm op
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* Replace RMSNorm module with RMSNorm op
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* Add AMP support
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* Do not save autograd context if grad mode is disabled

Debugging ONNX export tests.
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* Forward args in pre_forward func to base op class
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* Update to use QuantizedTensor class
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* Apply suggestions from code review
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* Review suggestions from @ptrendx

Rename "CastFloat8" op to "Quantize". Add more fine-grained control for SM margin. Add docs for legacy sequence_parallel kwarg.
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* Fix linter warnings
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* Use weight dtype as default compute dtype
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* Fix linter warnings
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* Debug CUDA graph support with operation-based API
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* Refactoring CUDA graph tests
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* Review suggestions from @ptrendx

Return default recipe from FP8GlobalStateManager.get_fp8_recipe if needed. Expand error message when failing to load FP8 state after capturing CUDA graph.
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* Avoid unnecessary recursion when saving/loading FP8 state
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* Fix circular import
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* Add op for in-place add
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* Add op for in-place add
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* Add op that adds extra output to fuser
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* Add fused op for GEMM+bias+add
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* Add fused op for dgrad+add
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* Add documentation
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* Fix linter warnings
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* Review suggestions from @ptrendx

Output tensor dtype and device take precedence over weight tensor in linear functional API. Move some index calculation to fuser constructor. Avoid some unnecessary dereferences.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Debug test failures
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* Update transformer_engine/pytorch/ops/fuser.py
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* Add basic infrastructure for Sequential module
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* Add linear op
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* Add FP8 support in linear op

Runs, but need to validate. Runtime errors with non-FP8 params and FP8 compute, or FP8 params and non-FP8 compute.
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* Add reshape op and unit test
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* Add bias op
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* Add unfused linear op

Test does not pass with FP8.
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* Debug unfused linear op
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* Add test for linear+bias op
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* Add separate abstract classes for unfused and fused ops
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* Consolidate unfused ops in submodule
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* Add linear-bias fused op
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* Use fused cast-transpose in linear ops
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* Disable GEMM+bias fusion with FP32 activations

Not supported by cuBLAS.
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* Add parallel unit test for unfused linear op
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* Refactor parallel tests to reduce job launches
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* Add all-reduce, all-gather, and reduce-scatter ops
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* Remove unused file
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* Debug multi-GPU FP8 test
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* Add support for FP8 scale updates

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* Add license boilerplate
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* Fuse GEMM+bias in row TP

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* Rename pipeline to fuser

Expand documentation
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* Tweak documentation
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* Preserve cached FP8 transpose between ops
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* Add option for fused wgrad accumulation
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* Directly output FP8 from linear if needed
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* Fix cuDNN front-end commit
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* Use updated FP8 tensor API for transpose caching
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* Use updated API for FP8 scale updates
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* Add tests for non-default FP8 recipes
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* Rename UnfusedOperation to BasicOperation
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* Add unit test to check amax reduction with fusable op
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* Operator autograd state no longer needs to be initialized
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* Initial functional implementation of linear op
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* Debug fused linear+bias op
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* Remove autograd context from functional linear impl
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* Use functional linear impl in fused linear+bias op
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* Rename subdirectory from "fuser" to "ops"

Avoid confusion with kernel fusers and graph compilers.
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* Update with Float8Tensor changes in #820
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* Remove unnecessary CPU overheads
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* Correctly pass FP8 metadata from next op
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* Fix linter errors
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* Add convenience functions to manipulate Sequential class
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* Update name of PyTorch extensions module
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* Clear saved tensor data in linear op after bprop
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* Fix Pylint error
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* Fix test name in QA script
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* Update name of PyTorch extensions module
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* Run distributed tests even when only 1 GPU is available
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* Only run distributed tests with 2 GPUs if there are >=2 GPUs
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* Review suggestions from @sudhakarsingh27 and @ksivaman

Fix spelling of "fusible". Avoid "input" name in internal APIs.
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* Update transformer_engine/pytorch/ops/__init__.py
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