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Avoid registering FP8 recipe update in ops without backward pass
Signed-off-by: Tim Moon <tmoon@nvidia.com>
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* Update amax pointers when reallocating amax history in fusible ops
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* Update weight tensor quantizer when recipe state is reset
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Fix merge conflict bug with clearing op outputs
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* Mark output tensors as not deletable in backward
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* Add `in_place` kwarg to `MakeExtraOutput`
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* Rename `AddInPlace` to `AddExtraInput` and add an `in_place` kwarg
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* Fix bug where TE ops were not updating fp8_meta dicts
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* Rename reset_recipe_state function
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* Update error message when initializing meta device quantized weight without recipe
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* Refactor _OperationFuserAutogradFunction.forward to use less parameters
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(cherry picked from commit f8f59b1bb184e89468058521df4cfff029ad909c)

* Rename `BackwardBiasActivation` to `BackwardActivationBias`
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* Use forward operation order in backward fused operations
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(cherry picked from commit 2d37a9385069b066e6cdeff3eb9173c2079cb791)

* Rename `prev_op_grad_input_quantizer` to `prev_op_grad_output_quantizer`
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* Make OperationFuser persistent
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* Distribute extra inputs to and collect extra outputs from multiple module groups in Sequential
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* Take requires_grad into account when fusing operations
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* Change get_quantizer to return None if no quantization recipe is used
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* Refactor pre_first_forward
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* Fix for failing `test_make_graphed_callables[fp8_recipe0-*-True-*-linear_op]`
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* Fix linting errors
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* Fix fp8 meta tensors in CUDA Graph capture
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* Fix failing distributed userbuffers tests
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* Fix clearing tensor data in backward removing is_first_op
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* Misc fixes
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* 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
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* 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
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* 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
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* 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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* Flatten basic op params during fuser init
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(cherry picked from commit 949abe97070721b1da5117903067608250f5fb61)

* Add caching for is_non_tn_fp8_gemm_supported
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(cherry picked from commit fd830ae24ffbd2d0727010b1a8a119ca72f61ce5)

* Pass fuser to _OperationFuserAutogradFunction.forward and moving computation to __init__
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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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* 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

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* Debug bug with incorrect bias pointers in UB GEMM

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* Add Userbuffers support for linear dgrad

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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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* 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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* 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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* 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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* 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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* 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 documentation
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* Fix linter warnings
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* Review suggestions from @ptrendx

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* Debug test failures
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* Add basic infrastructure for Sequential module
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* Add linear op
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* Disable GEMM+bias fusion with FP32 activations

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