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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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* Use internal tensors

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* Flatten basic op params during fuser init
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* Add caching for is_non_tn_fp8_gemm_supported
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* Pass fuser to _OperationFuserAutogradFunction.forward and moving computation to __init__
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fix saved_tensors
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* Avoid `parameters` function in op backward pass
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* Add Userbuffers support for column TP linear layer
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* Fix missing imports
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* Add layer norm op
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* Fix linter warnings
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* Do not save autograd context if grad mode is disabled

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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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* 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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* Review suggestions from @ptrendx

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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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* 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 separate abstract classes for unfused and fused ops
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* Consolidate unfused ops in submodule
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* Disable GEMM+bias fusion with FP32 activations

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* Refactor parallel tests to reduce job launches
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* Remove unused file
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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

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* Fix cuDNN front-end commit
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